Pathway specific markers for diagnosing irritable bowel syndrome

ABSTRACT

The present invention provides methods for aiding in the diagnosis of irritable bowel syndrome (IBS) in an individual. In particular, the present invention is useful for determining whether the individual does not have either celiac disease or inflammatory bowel disease (IBD), and has IBS and/or a subtype thereof. Thus, the present invention provides an accurate diagnostic prediction of IBS and is useful for guiding treatment decisions.

CROSS-REFERENCES TO RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.14/938,729, filed Nov. 11, 2015, which is a continuation ofPCT/IB2014/061636, filed May 22, 2014, which application claims priorityto U.S. Provisional Application No. 61/827,506, filed May 24, 2013, thedisclosures of which are hereby incorporated by reference in itsentirety for all purposes. This application incorporates by referencePCT/IB2014/061634.

BACKGROUND OF THE INVENTION

Irritable bowel syndrome (IBS) is the most common of allgastrointestinal disorders, affecting 10-20% of the general populationand accounting for more than 50% of all patients with digestivecomplaints. However, studies suggest that only about 10% to 50% of thoseafflicted with IBS actually seek medical attention. Patients with IBSpresent with disparate symptoms such as, for example, abdominal painpredominantly related to defecation, diarrhea, constipation oralternating diarrhea and constipation, abdominal distention, gas, andexcessive mucus in the stool. More than 40% of IBS patients havesymptoms so severe that they have to take time off from work, curtailtheir social life, avoid sexual intercourse, cancel appointments, stoptraveling, take medication, and even stay confined to their house forfear of embarrassment. The estimated health care cost of IBS in theUnited States is $8 billion per year (Talley et al., Gastroenterol.,109:1736-1741 (1995)).

IBS patients are classified into three groups according to theirpredominant bowel symptoms: constipation-predominant IBS (IBS-C),diarrhea-predominant IBS (IBS-D) and IBS with alternating symptoms ofdiarrhea and constipation (IBS-M), and unsubtyped IBS (IBS-U). Incurrent clinical practice, diagnosis of IBS is based on the Rome IIIcriteria and according to the symptoms presented by the patients plusthe exclusion of other GI disorders. There are no specific biological,radiographic, endoscopic or physiological biomarkers that can be used toidentify this disorder.

Irritable bowel syndrome is a heterogeneous gastrointestinal (GI)function disorder. There is increasing evidence pointing to theinvolvement of the immune system in its pathogenesis. GI infection maybe a triggering factor for causing the onset of IBS symptoms. On theother hand, IBS is often described as a “brain-gut disorder”.Alterations in GI motility and secretion mediated by dysregulation ofthe 5-HT signaling pathway may underlie the irregularities in bowelhabits. Activation of mast cells in proximity to colonic nervescorrelated with the abnormal pain experienced by patients with IBS. Mastcells are well known to be capable of producing and releasing a varietyof inflammatory mediators upon activation. However, it is not clear howthese different pathways communicate with each other and whether theirinteractions behave in the same manner in IBS patients as it is inhealthy subjects.

The precise pathophysiology of IBS remains to be elucidated. While gutdysmotility and altered visceral perception are considered importantcontributors to symptom pathogenesis (Quigley, Scand. J. Gastroenterol.,38(Suppl. 237):1-8 (2003); Mayer et al., Gastroenterol., 122:2032-2048(2002)), this condition is viewed as a stress-related disordercharacterized by disturbed brain-gut communication, enteric infection,intestinal inflammation, and/or altered microbiota (see, FIG. 1).Recently, roles for enteric infection and intestinal inflammation havealso been proposed. Studies have documented the onset of IBS followingbacteriologically confirmed gastroenteritis, while others have providedevidence of low-grade mucosal inflammation (Spiller et al., Gut,47:804-811 (2000); Dunlop et al., Gastroenterol., 125:1651-1659 (2003);Cumberland et al., Epidemiol. Infect., 130:453-460 (2003)) and immuneactivation (Gwee et al., Gut, 52:523-526 (2003); Pimentel et al., Am. J.Gastroenterol., 95:3503-3506 (2000)) in IBS. The enteric flora (e.g.,gut microbiome) has also been implicated, and a recent studydemonstrated the efficacy of the probiotic organism Bifidobacterium intreating the disorder through modulation of immune activity (Simren etal., Gut, 62:159-176 (2013)).

There is a growing body of evidence supporting the role of antimicrobialantibodies, stress hormones, inflammatory cytokines, and mast cellmarkers in various intestinal diseases or disorders. For instance, theantimicrobial antibodies OmpC, Cbir1, FlaX and Fla2 have been proven tobe valuable biomarkers of inflammatory bowel disease (IBD). Subsets ofantibodies to Escherichia coli K12 proteins (e.g., Era, FocA, FrvX,GabT, YbaN, YcdG, YhgN, and YidX) can be used to distinguish betweenindividuals with Crohn's Disease (CD) and healthy controls, and betweenindividuals with CD and ulcerative colitis (Chen et al., Mol. CellProteomics, 8:1765-1776, (2009)). Individuals with post-infectious smallintestine bacterial outgrowth (SIBO) associated with IBS which is oftencaused by infection from Campylobacter jejuni (C. jejuni, Cj),Escherichia coli (E. coli, Ec), Salmonella enteritidis (S. enteritidis,Se), Shigella flexneri (S. flexneri, Sf), may possess antibodies againstflagellin proteins of the infecting bacteria (Spiller R and Garsed K.,Gastroenterology, 136:1979-1988 (2009)).

Increased mast cell infiltration and activation in distal gut segmentsare associated with symptom onset and severity of IBS. These cells arealso implicated in the elevated response of visceral afferent nerves tomucosal stimulus in IBS patients. Mast cell hyperplasia is commonlyobserved following infection by these bacteria in both post-infectiousIBS and non-post-infectious IBS. Measurements of mast cell markers suchas β-tryptase, histamine and prostaglandin E2 (PGE2) have importantimplications in the clinical diagnosis of IBS. Detailed methods of usingmast cell markers to aid in the diagnosis of IBS are described in U.S.Pat. Nos. 8,114,616 and 8,709,733, the disclosures of which are herebyincorporated by reference in their entireties for all purposes.

IBS patients typically experience abnormal gut motility and visceralhypersensitivity mediated by the brain-gut axis and the gut microbiome(FIG. 1). In stress-sensitive disorders including IBS, stress hormonesof the hypothalamic-pituitary-adrenal axis (HPA) axis, such as guthormones, serotonin, adrenocorticotropin hormone (ACTH), cortisol,corticotropin-releasing hormone, and catecholamine are released, thuscontrolling the physiological function of, for example, the gut.Dysregulation of the brain-gut axis including the metabolite drivenpathways, such as the tryptophan pathway, kynurenine pathway andserotonin pathway (FIG. 2) can adversely affect gastrointestinalfunction by decreasing motility and increasing pain or discomfort.Therapeutics drugs directed to the serotonin pathway are currently underinvestigation for the treatment of IBS. Dysregulation of intestinal bileacid secretion and absorption is also associated with IBS (FIG. 3). Somestudies have also shown that gastrointestinal function is affected bythe gut microbiome (FIG. 4). For instance, diet, antibiotics, pathogens,and the host's immune response can change the gut's microbiomecommunity, which in turn, can alter intestinal function.

In view of the foregoing, there is a need in the art for methods fordiagnosing IBS in an individual by monitoring the brain-gut-microbiomeaxis. The present invention satisfies this and other needs.

BRIEF SUMMARY OF THE INVENTION

In some aspects, provided herein is a method for aiding in the diagnosisof irritable bowel syndrome (IBS) and/or a clinical subtype thereof in asubject.

The method comprises:

-   -   (a) detecting in a sample a panel of markers to rule-out a        diagnosis of inflammatory bowel disease and celiacs disease        (CD); and    -   (b) detecting in the sample a panel of markers to rule-in a        diagnosis of IBS.

In certain instances, the method comprises obtaining one or more of thefollowing scores (a)-(h): (a) detecting in a sample obtained from saidsubject the presence or absence of an anti-gliadin IgA antibody, ananti-gliadin IgG antibody, an anti-tissue transglutaminase (tTG)antibody, and an anti-endomysial antibody to obtain a celiac disease(CD) score; (b) detecting in said sample the presence or level orgenotype of at least each of the following markers to obtain aninflammatory bowel disease (IBD) score: (i) the presence or level ofeach of the serological markers ASCA-A, ASCA-G, ANCA, pANCA, anti-OmpCantibody, anti-CBir1 antibody, anti-FlaX antibody, and anti-A4-Fla2antibody; (ii) the presence or level of each of the inflammation markersVEGF, ICAM, VCAM, SAA, and CRP; and (iii) the genotype of each of thegenetic markers ATG16L1, ECM1, NKX2-3, and STAT3; (c) detecting in saidsample the level (e.g., concentration) of at least one bacterial antigenantibody marker to obtain a microbiome score; (d) detecting in saidsample the level (e.g., concentration) of at least one mast cell markerto obtain a mast cell score; (e) detecting in said sample the level(e.g., concentration) of at least one inflammatory cell marker to obtainan inflammatory score; (f) detecting in said sample the level (e.g.,concentration) of at least one bile acid malabsorption (BAM) marker toobtain a BAM score; (g) detecting in said sample the level (e.g.,concentration) of at least one kynurenine marker to obtain an oxidativestress score; (h) detecting in said sample the level (e.g.,concentration) of at least one serotonin marker to obtain a serotoninscore; (j) if said sample is a non-CD sample, then applying a randomforest statistical analysis to said IBD score to obtain a decisionwhether the sample is an IBD sample or a non-IBD sample; (k) if saidsample is a non-IBD sample, then applying a statistical algorithm tosaid microbiome score, said mast cell score, said inflammatory score,said BAM score, said oxidative stress score, and said serotonin score toobtain a disease score; and (l) determining a diagnosis of IBS in saidsubject based on a statistical algorithm that generates a probability ofhaving IBS based the disease score and a diagnostic model comprising amicrobiome score, mast cell score, an inflammatory score, a bile acidmalabsorption score, an oxidative stress score, and a serotonin scorefrom a retrospective cohort of patients.

In some embodiments, the at least one bacterial antigen antibody markeris selected from the group consisting of an anti-Fla1 antibody,anti-Fla2 antibody, anti-FlaA antibody, anti-FliC antibody, anti-FliC2antibody, anti-FliC3 antibody, anti-YBaN1 antibody, anti-ECFliCantibody, anti-Ec0FliC antibody, anti-SeFljB antibody, anti-CjFlaAantibody, anti-CjFlaB antibody, anti-SfFliC antibody, anti-CjCgtAantibody, anti-Cjdmh antibody, anti-CjGT-A antibody, anti-EcYidXantibody, anti-EcEra antibody, anti-EcFrvX antibody, anti-EcGabTantibody, anti-EcYedK antibody, anti-EcYbaN antibody, anti-EcYhgNantibody, anti-RtMaga antibody, anti-RbCpaF antibody, anti-RgPilDantibody, anti-LaFrc antibody, anti-LaEno antibody, anti-LjEFTuantibody, anti-BjOmpa antibody, anti-PrOmpA antibody, anti-Cp10bAantibody, anti-CpSpA antibody, anti-EfSant antibody, anti-LmOspantibody, anti-STET-2 antibody, anti-Cpatox antibody, anti-Cpbtoxantibody, anti-EcSta2 antibody, anti-Ec0Stx2A antibody, anti-CjcdtB/Cantibody, anti-CdtcdA/B antibody, and combinations thereof.

In some embodiments, the at least one mast cell marker is selected fromthe group consisting of β-tryptase, histamine, prostaglandin E2 (PGE2),and combinations thereof.

In some embodiments, the at least one inflammatory marker is selectedfrom the group consisting of CRP, ICAM, VCAM, SAA, GROα, andcombinations thereof.

In some embodiments, the at least one bile acid malabsorption marker isselected from the group consisting of 7α-hydroxy-4-cholesten-3-one,FGF19, and a combination thereof.

In some embodiments, the at least one kynurenine marker is selected fromthe group consisting of kynurenine (K), kynurenic acid (KyA),anthranilic acid (AA), 3-hydroxykynurenine (3-HK), 3-hydroxyanthranilicacid (3-HAA), xanthurenic acid (XA), quinolinic acid (QA), tryptophan,5-hydroxytryptophan (5-HTP), and combinations thereof.

In some embodiments, the at least one serotonin markers is selected fromthe group consisting of serotonin (5-HT), 5-hydroxyindoleacetic acid(5-HIAA), serotonin-O-sulfate, serotonin-O-phosphate, and combinationsthereof.

In some embodiments, the diagnostic model is established using aretrospective cohort with known outcomes of IBS and healthy controls. Inother embodiments, the diagnostic model is established using aretrospective cohort with known outcomes of a clinical subtype of IBSand healthy controls.

In some embodiments, the method further comprises classifying adiagnosis of IBS as IBS-constipation (IBS-C), IBS diarrhea (IBS-D),IBS-mixed (IBS-M), IBS-alternating (IBS-A), or post-infectious (IBS-PI).

In some embodiments, the level of said bacterial antigen antibodymarker, said mast cell marker, said inflammatory cell marker, said BAMmarker, said kynurenine marker or said serotonin marker is independentlydetected with a hybridization assay, amplification-based assay,immunoassay, immunohistochemical assay, or a mobility assay. In someinstances, the hybridization assay comprises an ELISA or a CEER™ assay.

In some embodiments, the sample is selected from the group consisting ofwhole blood, plasma, serum, saliva, urine, stool, tears, any otherbodily fluid, a tissue sample, and a cellular extract thereof. In someinstances, the sample is serum.

In some embodiments, at least 1, 2, 3, 4, 5, or 6 of the followingscores are measured: microbiome score, a mast cell score, aninflammatory score, a bile acid malabsorption score, an oxidative stressscore, and a serotonin score.

In some aspects, provided herein is a method for aiding in the diagnosisof irritable bowel syndrome (IBS) and/or a clinical subtype thereof in asubject. The method comprises obtaining one or more of the following (a)through (f) scores: (a) detecting in a sample obtained from said subjectthe level of at least one bacterial antigen antibody marker to obtain amicrobiome score; (b) detecting in said sample the level of at least onemast cell marker to obtain a mast cell score; (c) detecting in saidsample the level of at least one inflammatory cell marker to obtain aninflammatory score; (d) detecting in said sample the level of at leastone bile acid malabsorption (BAM) marker to obtain a BAM score; (e)detecting in said sample the level of at least one kynurenine marker toobtain an oxidative stress score; (f) detecting in said sample the levelof at least one serotonin marker to obtain a serotonin score; (g)applying a statistical algorithm to said microbiome score, said mastcell score, said inflammatory score, said BAM score, said oxidativestress score, and said serotonin score to obtain a disease score; and(h) determining a diagnosis of IBS in said subject based on astatistical algorithm that generates a probability of having IBS basedthe disease score and a diagnostic model comprising a microbiome score,mast cell score, an inflammatory score, a bile acid malabsorption score,an oxidative stress score, and a serotonin score from a retrospectivecohort.

In some embodiments, the at least one bacterial antigen antibody markeris selected from the group consisting of an anti-Fla1 antibody,anti-Fla2 antibody, anti-FlaA antibody, anti-FliC antibody, anti-FliC2antibody, anti-FliC3 antibody, anti-YBaN1 antibody, anti-ECFliCantibody, anti-Ec0FliC antibody, anti-SeFljB antibody, anti-CjFlaAantibody, anti-CjFlaB antibody, anti-SfFliC antibody, anti-CjCgtAantibody, anti-Cjdmh antibody, anti-CjGT-A antibody, anti-EcYidXantibody, anti-EcEra antibody, anti-EcFrvX antibody, anti-EcGabTantibody, anti-EcYedK antibody, anti-EcYbaN antibody, anti-EcYhgNantibody, anti-RtMaga antibody, anti-RbCpaF antibody, anti-RgPilDantibody, anti-LaFrc antibody, anti-LaEno antibody, anti-LjEFTuantibody, anti-BjOmpa antibody, anti-PrOmpA antibody, anti-Cp10bAantibody, anti-CpSpA antibody, anti-EfSant antibody, anti-LmOspantibody, anti-STET-2 antibody, anti-Cpatox antibody, anti-Cpbtoxantibody, anti-EcSta2 antibody, anti-Ec0Stx2A antibody, anti-CjcdtB/Cantibody, anti-CdtcdA/B antibody, and combinations thereof.

In some embodiments, the at least one mast cell marker is selected fromthe group consisting of β-tryptase, histamine, prostaglandin E2 (PGE2),and combinations thereof.

In some embodiments, the at least one inflammatory marker is selectedfrom the group consisting of CRP, ICAM, VCAM, SAA, GROα, andcombinations thereof.

In some embodiments, the at least one bile acid malabsorption marker isselected from the group consisting of 7α-hydroxy-4-cholesten-3-one,FGF19, and a combination thereof.

In some embodiments, the at least one kynurenine marker is selected fromthe group consisting of kynurenine (K), kynurenic acid (KyA),anthranilic acid (AA), 3-hydroxykynurenine (3-HK), 3-hydroxyanthranilicacid (3-HAA), xanthurenic acid (XA), quinolinic acid (QA), tryptophan,5-hydroxytryptophan (5-HTP), and combinations thereof.

In some embodiments, the at least one serotonin markers is selected fromthe group consisting of serotonin (5-HT), 5-hydroxyindoleacetic acid(5-HIAA), serotonin-O-sulfate, serotonin-O-phosphate, and combinationsthereof.

In some embodiments, the diagnostic model is established using aretrospective cohort with known outcomes of IBS and healthy controls. Inother embodiments, the diagnostic model is established using aretrospective cohort with known outcomes of a clinical subtype of IBSand healthy controls.

In some embodiments, the method further comprises classifying adiagnosis of IBS as IBS-constipation (IBS-C), IBS diarrhea (IBS-D),IBS-mixed (IBS-M), IBS-alternating (IBS-A), or post-infectious (IBS-PI).

In some embodiments, at least 1, 2, 3, 4, 5, or 6 of the followingscores are measured: microbiome score, a mast cell score, aninflammatory score, a bile acid malabsorption score, an oxidative stressscore, and a serotonin score.

In some embodiments, the presence or absence or level of said bacterialantigen antibody marker, said mast cell marker, said inflammatory cellmarker, said BAM marker, said kynurenine marker or said serotonin markeris independently detected with a hybridization assay,amplification-based assay, immunoassay, immunohistochemical assay, or amobility assay. In some instances, the hybridization assay comprises anELISA or a CEER™ assay.

Other objects, features, and advantages of the present invention will beapparent to one of skill in the art from the following detaileddescription and figures.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates the brain-gut-microbiome axis and the complexpathophysiology of IBS. It highlights some of the biomarkers describedherein that can be used for the diagnosis of IBS and/or subtypesthereof.

FIG. 2 shows metabolite driven pathways and enzymes that aredysregulated in IBS patients. In patients with IBS-D, tryptophan levelsare increased while kynurenic acid (KA), 3-hydroxykynurenine (3-HK), and3-hydroantrhanilic acid (3-HAA) levels are decreased. In addition, theactivity of the enzymes, such as tryptophan dioxygenase/indoleamine2,3-dioxygenase (TDO/IDO), kynurenine hydroxylase, and kynureninase arelower (decreased).

FIG. 3 shows a diagram of the intestinal bile acid secretion andabsorption pathway.

FIG. 4 illustrates the diversity of the gut microbiome.

FIG. 5 illustrates an exemplary embodiment of the method of the presentinvention.

FIGS. 6A and 6B show exemplary embodiments of the statistical analysisof the biomarkers described herein. FIG. 6A shows that bacterial antigenantibody markers and one inflammatory marker are predictive of IBS. FIG.6B shows that bacterial antigen antibody markers, one inflammatorymarker, and one mast cell marker are predictive of IBS.

FIG. 7 shows the tree-building process with one inflammatory marker(sVCAM1), and several microbiome markers (EcGabT, Ec0FliC, EcEra, andEcYbaN).

FIGS. 8A-8N shows the levels of specific bacterial antigen antibodymarkers in healthy controls and IBS patients. The microbiome markersinclude EcEra (FIG. 8A), EcFliC (FIG. 8B), EcFrvX (FIG. 8C), EcGabT(FIG. 8D), EcYedK (FIG. 8E), EcYbaN (FIG. 8F), EcOFliC (FIG. 8G), CjFlaA(FIG. 8H), CjFlaB (FIG. 8I), CjGT-A (FIG. 8J), CjCgtA (FIG. 8K), Cjdmh(FIG. 8L), SeFljB (FIG. 8M), and SJFliC (FIG. 8N).

FIGS. 9A-9C show graphs used to calculated a biomarker score and a scorepercentile. FIGS. 9A and 9B shows that the weights for each biomarker(e.g., EcEra and EcFliC) are determined from coefficients of theregression or slope between the disease cohort and the healthy cohort.The lines in FIGS. 9A and 9B represent βs. A positive slope indicatesIBS and negative slope indicates healthy control. For each individual,the weighted quartile sum score is represented as Σβ* quartile over allmarkers, wherein β represents the coefficients form the regression orslope between the cohorts (FIG. 9C). The coefficients are adjusted forthe presence of other markers. FIG. 9C shows an exemplary embodiment ofthe quartile analysis described herein.

FIGS. 10A and 10B show graphs of the microbiome scores (FIG. 10A) andthe microbiome score percentiles (FIG. 10B) for the subjects in thehealthy control cohort. The graphs also show the microbiome score forone representative IBS patient relative to the healthy controls.

FIGS. 11A and 11B show graphs of the microbiome scores (FIG. 11A) inhealthy controls and IBS-D/M patients and the distribution of the scores(FIG. 11B).

FIGS. 12A-12E show the level of different bacterial antigen antibodymarkers in cohort #1 containing healthy controls and IBS-DIM patients.The markers shown are LaEno (FIG. 12A), LaFrc (FIG. 12B), LjEFtu (FIG.12C), BJOmpA (FIG. 12D), and PrOmpA (FIG. 12E).

FIGS. 13A-13K show the level of different bacterial antigen antibodymarkers in cohort #2 containing healthy controls and IBS patientsincluding those with IBS-C and IBS-D. The markers shown are EcGabT (FIG.13A), EcEra (FIG. 13B), EcOFliC (FIG. 13C), SjFliC (FIG. 13D), CjFlaB(FIG. 13E), CjFlaA (FIG. 13F), EcFliC (FIG. 13G), RtMaga (FIG. 13H),RtPilD (FIG. 13I), and RbCpaF (FIGS. 13J and 13K).

FIGS. 14A-14G show the level of different bacterial antigen antibodymarkers in cohort #3 which includes healthy controls and IBS patients.The markers shown are SjFliC (FIG. 14A), CjFlaB (FIG. 14B), CjFlaA (FIG.14C), EcFliC (FIG. 14D), EcGabT (FIG. 14E), EcEra (FIG. 14F), andEcOFliC (FIG. 14G).

FIGS. 15A-15C show the level of serotonin in healthy controls and IBSpatients as determined by HPLC. FIG. 15A shows a graph of serotoninlevels. FIG. 15B shows a chromatogram of serotonin and serotoninmetabolites. FIG. 15C provides a table of serotonin levels.

FIGS. 16A-16B show the level of serotonin in healthy controls and IBS-Dpatients as determined by a novel competitive ELISA. FIG. 16A shows agraph of serotonin levels in IBS-D patients. FIG. 16B provides a tableof the results.

DETAILED DESCRIPTION OF THE INVENTION

I. Introduction

Diagnosing a patient as having irritable bowel syndrome can bechallenging due to the similarity in symptoms between IBS and otherintestinal diseases or disorders. Biomarker-based assays can providerapid and accurate diagnostic methods to distinguish IBS from otherdiseases and disorders.

Although the precise pathophysiology of IBS remains to be elucidated. Itis believed that IBS is caused, in part, by dysregulation of the host'smicrobiome in the gut and stress hormones. Studies have shown that thegastrointestinal microbiota can influence the host and results inmucosal inflammation and immune activation, and that cortisol levels canbe high in women with IBS (Heitkemper et al., Am J Gastroenterol,91(5):906-13 (1996)).

Observations supporting this theory include the finding that anincreased number of mast cells can be found in the gastrointestinalmucosa of patients diagnosed with IBS (Guilarte, M. et al., Gut 56,203-209 (2007); Walker, M. M. et al., Pharmacol. Ther., 29, 765-773(2009); Akbar, A. et al., Gut 57, 923-929 (2008); Barbara, G. et al.,Gastroenterology 126, 693-702 (2004); Barbara, G. et al.,Gastroenterology 132, 26-37 (2007); Cremon, C. et al., Am. J.Gastroenterol. 104, 392-400 (2009); and O'Sullivan, M. et al.,Neurogastroenterol. Motif., 12, 449-457 (2000)). Similarly, some studieshave also found that levels of mediators released from these cells,including histamine and serine proteases (e.g., tryptase), are found inthe colonic mucosa of IBS patients (Buhner et al., Gastroenterology,137(4), (2009)); Barbara et al., Gastroenterology, 122(4), Suppl. 1:A-276, (2002)).

The human gastrointestinal microbiota includes at least 1,000 species ofbacteria, and about 10¹⁴ individual bacterial cells from about 160different species inhabit each individual's intestine (Qin et al.,Nature, 464:59-65 (2010)). It has been theorized that the host's (e.g.,individual's) genetic and immune composition as well as environmentalfactors influence the gastrointestinal microbiota, which in turn shapesthe host's immunity and physiology within the gastrointestinal system.This theory suggests that a healthy individual (e.g., free of intestinaldisorders or disease) maintains a symbiotic relationship with themicrobiota colonizing his/her intestines, while an individual with IBShas an imbalance in this microbiota-immune interaction.

The serotonin pathway plays a critical role in the regulation ofgastrointestinal motility, secretion, and sensation. Imbalances in thispathway within the enteric nervous system have been associated withvarious disorders, such as IBS, functional dyspepsia, non-cardiac chestpain, autism, and gastric ulcer formation. Significant alterations ofthe tryptophan/serotonin/kynurenine metabolic and catabolic pathways(FIG. 2) have been implicated in IBS-D (Christmas et al., NutritionResearch, 2010, 30:678-688).

The present invention provides methods for diagnosing irritable bowelsyndrome (IBS) in a subject. The methods include measuring the level ofan array of celiac disease (CD) markers, IBD markers, microbiomemarkers, mast cell markers, inflammatory markers, bile acidmalabsorption markers, kynurenine markers, and serotonin markers in abiological sample taken from the subject; generating a series ofbiomarker scores; and using an algorithm to determine whether thesubject does not have CD or IBD and has an increased likelihood ofhaving IBS compared to being a healthy control. The present inventionalso provides methods and assays for measuring the level of variousbiomarkers.

II. Definitions

As used herein, the following terms have the meanings ascribed to themunless specified otherwise.

The terms “irritable bowel syndrome” and “IBS” includes a group offunctional bowel disorders characterized by one or more symptomsincluding, but not limited to, abdominal pain, abdominal discomfort,change in bowel pattern, loose or more frequent bowel movements,diarrhea, and constipation, typically in the absence of any apparentstructural abnormality. There are at least three forms of IBS, dependingon which symptom predominates: (1) diarrhea-predominant (IBS-D); (2)constipation-predominant (IBS-C); and (3) IBS with alternating stoolpattern (IBS-A). IBS can also occur in the form of a mixture of symptoms(IBS-M). There are also various clinical subtypes of IBS, such aspost-infectious IBS (IBS-PI).

The terms “celiac disease” and “CD” refer to a disorder of theintestinal mucosa that is typically associated with villous atrophy,crypt hyperplasia, and/or inflammation of the mucosal lining of thesmall intestine. In addition to the malabsorption of nutrients,individuals with Celiac disease are at risk for mineral deficiency,vitamin deficiency, osteoporosis, autoimmune diseases, and intestinalmalignancies (e.g., lymphoma and carcinoma). Without being bound by anyparticular theory, it is thought that exposure to proteins such asgluten (e.g., glutenin and prolamine proteins which are present inwheat, rye, barley, oats, millet, triticale, spelt, and kamut), in theappropriate genetic and environmental context, is responsible forcausing Celiac disease.

The term “inflammatory bowel disease” or “IBD” includes gastrointestinaldisorders such as, e.g., Crohn's disease (CD), ulcerative colitis (UC),indeterminate colitis (IC), and IBD that is inconclusive for CD vs. UC(“Inconclusive”). Inflammatory bowel diseases (e.g., CD, UC, IC, andInconclusive) are distinguished from all other disorders, syndromes, andabnormalities of the gastroenterological tract, including irritablebowel syndrome (IBS). Detailed descriptions of methods for diagnosis IBSare found in, e.g., U.S. Pat. Nos. 7,873,479 and 8,715,943, the contentsare hereby incorporated by reference in their entirety for all purposes.

The terms “microbiota,” “microflora” and “microbiome” refer to thecommunity of living microorganisms that typically inhabits a bodilyorgan or part. Members of the gastrointestinal microbiota include, butare not limited to, microorganisms of the phyla of Firmicutes,Bacteroidetes, Proteobacteria, Epsilonproteobacteria, Fusobacteria,Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria,Verrumicrobia, Deltaproteobacteria, Unclassified near cyanobacteria, andActinobacteria; microorganisms of the Bacteroides, Prevotella orRuminococcus genera; microorganisms of the Bifidobacteria,Enterobacteraceae, Lactobacillus, Veillonella, Bacteoides,Streptococcus, Actinomycinaea, Helicobacter, Peptostreptococcus,Collinsella, Clostridium, Enterococcus, Coprococcus, Coprobacillus,Proteobacteria, Lactobacillus, Ruminococus, Eubacterium, Dorea,Acinetobacter, and Escherichia coli species; microorganisms of theRuminococcus torques, R. torques-like, Collinsella aerofaciens-like,Clostridium cocleatum, Eubacterium rectale, Clostridium coccoides,Rhinobatos productus types. In some instances, the gastrointestinalmicrobiota includes the mucosa-associated microbiota, which is locatedat the surface or apical end of the gastrointestinal tract, andluminal-associated microbiota, which is found in the lumen of thegastrointestinal tract.

The term “biomarker” or “marker” includes any diagnostic marker such asa biochemical marker, serological marker, genetic marker, microbialmarker or other clinical or echographic characteristic that can be usedto classify a sample from an individual as an IBS sample or to rule outone or more diseases or disorders associated with IBS-like symptoms in asample from an individual. The term “biomarker” or “marker” alsoencompasses any classification marker such as an antibody marker,biochemical marker, serological marker, genetic marker, hormonal marker,microbial marker, or other clinical or echographic characteristic thatcan be used to classify IBS into one of its various forms or clinicalsubtypes. Non-limiting examples of diagnostic markers suitable for usein the present invention are described below and include antibodiesagainst bacterial antigens, bacterial antigens, flagellins, cytokines,growth factors, stress hormones, anti-neutrophil antibodies,anti-Saccharomyces cerevisiae antibodies, antimicrobial antibodies,anti-tissue transglutaminase (tTG) antibodies, lipocalins, matrixmetalloproteinases (MMPs), tissue inhibitor of metalloproteinases(TIMPs), alpha-globulins, actin-severing proteins, S100 proteins,fibrinopeptides, calcitonin gene-related peptide (CGRP), tachykinins,ghrelin, neurotensin, serotonin, corticotropin-releasing hormone (CRH),serine proteases (e.g., β-tryptase, elastase), prostaglandin (e.g.,PGE2), histamine, C-reactive protein (CRP), lactoferrin,anti-lactoferrin antibodies, calprotectin, hemoglobin, NOD2/CARD15,serotonin reuptake transporter (SERT), tryptophan hydroxylase-1,5-hydroxytryptamine (5-HT), lactulose, and the like. In preferredembodiments, diagnostic markers suitable for use in the presentinvention are described herein and include, without limitation, anantibody that binds to a microbiota antigen selected from the groupconsisting of E. coli FliC, S. flexneri FliC, C. jejuni FlaA, C. jejuniFlaB, E. coli O157:H7 FliC, E. coli FrvX, E. coli GabT, C. jejuni81-045, C. jejuni 81-128, and C. jejuni 81-008, E. coli Era, E. coliFocA, E. coli FrvX, E. coli GabT, E. coli YbaN, E. coli YcdG, E. coliYhgN, E. coli YedK, E. coli YidX, L. acidophilus Frc, L. acidophilusEno, L. johnsonii EFTu, B. fragilis OmpA, Prevotella OmpA, C.perfringens 10bA, C. perfringens SpA, E. faecalis Sant, L. monocytogenesOsp, and mixtures thereof. Examples of classification markers include,without limitation, leptin, SERT, tryptophan hydroxylase-1,5-HT, antrummucosal protein 8, keratin-8, claudin-8, zonulin, corticotropinreleasing hormone receptor-1 (CRHR1), corticotropin releasing hormonereceptor-2 (CRHR2), β-tryptase, histamine, prostaglandin E2 (PGE2) andthe like. In some embodiments, diagnostic markers can be used toclassify IBS into one of its various forms or clinical subtypes. Inother embodiments, classification markers can be used to classify asample as an IBS sample or to rule out one or more diseases or disordersassociated with IBS-like symptoms. One skilled in the art will know ofadditional diagnostic and classification markers suitable for use in thepresent invention.

The term “estimate” refers to the estimated partial correlationcoefficient of a logistic regression model.

The “biological sample” includes any biological specimen obtained froman individual. Suitable samples for use in the present inventioninclude, without limitation, whole blood, plasma, serum, saliva, urine,stool (i.e., feces), tears, and any other bodily fluid, or a tissuesample (i.e., biopsy) such as a small intestine or colon sample, andcellular extracts thereof (e.g., red blood cellular extract). In apreferred embodiment, the sample is a blood, plasma, or serum sample. Ina more preferred embodiment, the sample is a serum sample. In certaininstances, the term “sample” includes, but is not limited to blood, bodytissue, blood serum, lymph fluid, lymph node tissue, spleen tissue, bonemarrow, or an immunoglobulin enriched fraction derived from one or moreof these tissues. The use of samples such as serum, saliva, and urine iswell known in the art (see, e.g., Hashida et al., J. Clin. Lab. Anal.,11:267-86 (1997)). One skilled in the art will appreciate that samplessuch as serum and blood samples can be diluted prior to the analysis ofmarker levels.

The term “individual,” “subject,” or “patient” typically refers tohumans, but also to other animals including, e.g., other primates,rodents, canines, felines, equines, ovines, porcines, and the like.

The term “classifying” includes “to associate” or “to categorize” asample with a disease state. In certain instances, “classifying” isbased on statistical evidence, empirical evidence, or both. In certainembodiments, the methods and systems of classifying use a so-calledtraining set of samples having known disease states. Once established,the training data set serves as a basis, model, or template againstwhich the features of an unknown sample are compared, in order toclassify the unknown disease state of the sample. In certain instances,classifying the sample is akin to diagnosing the disease state of thesample. In certain other instances, classifying the sample is akin todifferentiating the disease state of the sample from another diseasestate.

As used herein, the term “score” or “profile” includes any set of datathat represents the distinctive features or characteristics associatedwith a disease or disorder such as IBS. The term encompasses a “diseasescore” or “diagnostic score” that analyzes one or more diagnosticmarkers in a sample. For example, a “disease score” can include a set ofdata that represents the presence or level of one or more diagnosticmarkers associated with IBS.

In some embodiments, a panel for measuring one or more of the diagnosticmarkers and/or diagnostic model described above can be constructed andused for classifying the sample as an IBS sample or non-IBS sample. Oneskilled in the art will appreciate that the presence or level of aplurality of diagnostic markers can be determined simultaneously orsequentially, using, for example, an aliquot or dilution of theindividual's sample. In certain instances, the level of a particulardiagnostic marker in the individual's sample is considered to beelevated when it is at least about 10%, 15%, 20%, 25%, 50%, 75%, 100%,125%, 150%, 175%, 200%, 250%, 300%, 350%, 400%, 450%, 500%, 600%, 700%,800%, 900%, or 1000% greater than the level of the same marker in acomparative sample (e.g., a normal, GI control, IBD, and/or celiacdisease sample) or population of samples (e.g., greater than a medianlevel of the same marker in a comparative population of normal, GIcontrol, IBD, and/or celiac disease samples). In certain otherinstances, the level of a particular diagnostic marker in theindividual's sample is considered to be lowered when it is at leastabout 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%,70%, 75%, 80%, 85%, 90%, or 95% less than the level of the same markerin a comparative sample (e.g., a normal, GI control, IBD, and/or celiacdisease sample) or population of samples (e.g., less than a median levelof the same marker in a comparative population of normal, GI control,IBD, and/or celiac disease samples).

As used herein, the term “substantially the same amino acid sequence”includes an amino acid sequence that is similar, but not identical to,the naturally-occurring amino acid sequence. For example, an amino acidsequence that has substantially the same amino acid sequence as anaturally-occurring peptide, polypeptide, or protein can have one ormore modifications such as amino acid additions, deletions, orsubstitutions relative to the amino acid sequence of thenaturally-occurring peptide, polypeptide, or protein, provided that themodified sequence retains substantially at least one biological activityof the naturally-occurring peptide, polypeptide, or protein such asimmunoreactivity. Comparison for substantial similarity between aminoacid sequences is usually performed with sequences between about 6 and100 residues, preferably between about 10 and 100 residues, and morepreferably between about 25 and 35 residues. A particularly usefulmodification of a peptide, polypeptide, or protein of the presentinvention, or a fragment thereof, is a modification that confers, forexample, increased stability. Incorporation of one or more D-amino acidsis a modification useful in increasing stability of a polypeptide orpolypeptide fragment. Similarly, deletion or substitution of lysineresidues can increase stability by protecting the polypeptide orpolypeptide fragment against degradation.

The terms “complex,” “immuno-complex,” “conjugate,” and“immunoconjugate” include, but are not limited to, peptide or antigenbound (e.g., by non-covalent means) to an antibody or an antibodyfragment.

The term “monitoring the progression or regression of IBS” includes theuse of the methods, systems, and code of the present invention todetermine the disease state (e.g., presence or severity of IBS) of anindividual. In some embodiments, the methods, systems, and code of thepresent invention can be used to predict the progression of IBS, e.g.,by determining a likelihood for IBS to progress either rapidly or slowlyin an individual based on an analysis of diagnostic markers and/or theidentification or IBS-related symptoms. In other embodiments, themethods, systems, and code of the present invention can be used topredict the regression of IBS, e.g., by determining a likelihood for IBSto regress either rapidly or slowly in an individual based on ananalysis of diagnostic markers and/or the identification or IBS-relatedsymptoms.

The term “bacterial antigen antibody marker score, “bacterial antigenantibody score,” “microbiome marker score,” or “microbiome score”includes the level of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,34, 35, or more markers of an individual, wherein the markers can be abacterial antigen antibody marker, such as, but not limited to, anantibody that recognizes (e.g., specifically bind to, forms a complex)with a bacterial antigen, such as Fla1, Fla2, FlaA, FliC, FliC2, FliC3,YBaN1, ECFliC, Ec0FliC, SeFljB, CjFlaA, CjFlaB, SfFliC, CjCgtA, Cjdmh,CjGT-A, EcYidX, EcEra, EcFrvX, EcGabT, EcYedK, EcYbaN, EcYhgN, RtMaga,RbCpaF, RgPilD, LaFrc, LaEno, LjEFTu, BjOmpa, PrOmpA, Cp10bA, CpSpA,EfSant, LmOsp, STET-2, Cpatox, Cpbtox, EcSta2, Ec0Stx2A, CjcdtB/C,CdtcdA/B, and the like. A statistical analysis can transform the levelof the bacterial antigen antibody marker(s) into a bacterial antigenantibody marker profile. In some instances, a statistical analysis is aquartile score and the quartile score for each of the markers can besummed to generate a quartile sum score. In one aspect, a statisticalprocess comprising a single learning statistical classifier system isapplied to the data set of the bacterial antigen antibody marker profileto produce a statistically derived decision classifying a sample as anIBS sample or a non-IBS sample (e.g., healthy control sample) based uponthe bacterial antigen antibody marker profile, wherein the bacterialantigen antibody marker profile indicates the level of at least onebacterial antigen antibody marker.

The term “mast cell marker score” or “mast cell score” includes thelevel of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more markers of anindividual, wherein the markers can be a mast cell marker, such as, butnot limited to β-tryptase, histamine, and prostaglandin E2. Astatistical analysis transforms the level of the mast cell marker(s)into a mast cell marker score. In some instances, a statistical analysisis a quartile score and the quartile score for each of the markers canbe summed to generate a quartile sum score. In one aspect, a statisticalanalysis comprises a single learning statistical classifier system isapplied to the data set of the mast cell marker score to produce astatistically derived decision classifying a sample as an IBS sample ora non-IBS sample based upon the mast cell marker wherein the mast cellmarker score indicates the level of at least one mast cell marker.

The term “inflammatory cell marker score,” “inflammatory marker score”or “inflammatory score” includes the level of 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more marker of anindividual, wherein the marker can be an inflammatory cell marker, suchas, but not limited CRP, ICAM, VCAM, SAA, GROα, and combinationsthereof. A statistical analysis transforms the level of the inflammatorycell marker(s) into an inflammatory score. In some instances, astatistical analysis is a quartile score and the quartile score for eachof the markers can be summed to generate a quartile sum score. In oneaspect, a statistical analysis comprises a single learning statisticalclassifier system is applied to the data set of the inflammatory cellmarker score to produce a statistically derived decision classifying asample as an IBS sample or a non-IBS sample based upon the inflammatorycell marker wherein the inflammatory score indicates the level of atleast one inflammatory cell marker.

The term “kynurenine marker score,” “kynurenine score,” or “oxidativestress score” includes the level of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, 16, 17, 18, 19, 20 or more markers of an individual,wherein the markers can be a kynurenine cell marker, such as, but notlimited kynurenine (K), kynurenic acid (KyA), anthranilic acid (AA),3-hydroxykynurenine (3-HK), 3-hydroxyanthranilic acid (3-HAA),xanthurenic acid (XA), quinolinic acid (QA), tryptophan,5-hydroxytryptophan (5-HTP), and a combination thereof. A statisticalanalysis transforms the level of the kynurenine marker(s) into akynurenine score. In some instances, a statistical analysis is aquartile score and the quartile score for each of the markers can besummed to generate a quartile sum score. In one aspect, a statisticalanalysis comprises a single learning statistical classifier system isapplied to the data set of the kynurenine marker score to produce astatistically derived decision classifying a sample as an IBS sample ora non-IBS sample based upon the kynurenine marker wherein the kynureninescore indicates the level of at least one kynurenine marker.

The term “serotonin marker score” or “serotonin score” includes thelevel of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,19, 20 or more markers of an individual, wherein the markers can be aserotonin marker, such as, but not limited serotonin (5-HT) and5-hydroxyindoleacetic acid (5-HIAA), serotonin-O-sulfate,serotonin-O-phosphate, and combinations thereof. A statistical analysistransforms the level of the serotonin marker(s) into a serotonin score.In some instances, a statistical analysis is a quartile score and thequartile score for each of the markers can be summed to generate aquartile sum score. In one aspect, a statistical analysis comprises asingle learning statistical classifier system is applied to the data setof the serotonin marker score to produce a statistically deriveddecision classifying a sample as an IBS sample or a non-IBS sample basedupon the serotonin marker wherein the serotonin score indicates thelevel of at least one serotonin marker.

The term “inflammatory bowel disease marker score,” “inflammatory boweldisease score,” “IBD marker score” or “IBD score” includes the level of1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 ormore markers of an individual, wherein the markers can be an IBD marker,such as, but not limited, an anti-neutrophil cytoplasmic antibody(ANCA), an anti-Saccharomyces cerevisiae immunoglobulin G (ASCA-IgA), ananti-Saccharomyces cerevisiae immunoglobulin G (ASCA-IgG), an anti-outermembrane protein C (anti-OmpC) antibody, an anti-flagellin antibody, aperinuclear anti-neutrophil cytoplasmic antibody (pANCA), an anti-Fla2antibody, an anti-FlaX antibody, an anti-CBir antibody, ICAM-1, VCAM-1,VEGF, CRP, SAA, and combinations thereof. A statistical analysistransforms the level of the IBD marker(s) into an IBD score. Additionalgenetic markers of IBD include ATG16L1, ECM1, NKX2-3, STAT3, and SNPsthereof. In some instances, a statistical analysis is a quartile scoreand the quartile score for each of the markers can be summed to generatea quartile sum score. In one aspect, a statistical analysis comprises asingle learning statistical classifier system is applied to the data setof the IBD marker score to produce a statistically derived decisionclassifying a sample as an IBD sample or a non-IBD sample based upon theIBD marker wherein the IBD score indicates the level of at least one IBDmarker.

The term “bile acid malabsorption marker score,” “bile acidmalabsorption score” or “BAM score” includes the level of 1, 2 or moremarkers of an individual, wherein the markers can be a BAM marker, suchas, but not limited 7-α-hydroxy-4-cholesten-3-one and FGF19. Astatistical analysis transforms the level of the BAM marker(s) into aBAM score. In some instances, a statistical analysis is a quartile scoreand the quartile score for each of the markers can be summed to generatea quartile sum score. In one aspect, a statistical analysis comprises asingle learning statistical classifier system is applied to the data setof the BAM marker score to produce a statistically derived decisionclassifying a sample as an IBS sample or a non-IBS sample based upon theBAM marker wherein the BAM score indicates the level of at least one BAMmarker.

The term “celiac disease marker score,” “celiac disease score,” “CDmarker score” or “CD score” includes the level of 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more markers of anindividual, wherein the markers can be a CD marker, such as, but notlimited an anti-gliadin IgA antibody, an anti-gliadin IgG antibody, ananti-tissue transglutaminase (tTG) antibody, an anti-endomysialantibody, and combinations thereof. A statistical analysis transformsthe level of the CD marker(s) into an CD score. In some instances, astatistical analysis is a quartile score and the quartile score for eachof the markers can be summed to generate a quartile sum score. In oneaspect, a statistical analysis comprises a single learning statisticalclassifier system is applied to the data set of the CD marker score toproduce a statistically derived decision classifying a sample as a CDsample or a non-CD sample based upon the CD marker wherein the CD scoreindicates the level of at least one CD marker.

In quartile analysis, there are three numbers (values) that divide arange of data into four equal parts. The first quartile (also called the“lower quartile”) is the number below which lies the 25 percent of thebottom data. The second quartile (the “median quartile”) divides therange in the middle and has 50 percent of the data below it. The thirdquartile (also called the “upper quartile”) has 75 percent of the databelow it and the top 25 percent of the data above it. As a non-limitingexample, quartile analysis can be applied to the concentration level ofa marker such as an antibody or other protein marker described herein,such that a marker level in the first quartile (<25%) is assigned avalue of 1, a marker level in the second quartile (25-50%) is assigned avalue of 2, a marker level in the third quartile (51%-<75%) is assigneda value of 3, and a marker level in the fourth quartile (75%-100%) isassigned a value of 4.

As used herein, “quartile sum score” or “QSS” includes the sum ofquartile scores for all of the markers of interest. As a non-limitingexample, a quartile sum score for a panel of 6 markers may range from6-24, wherein each of the individual markers is assigned a quartilescore of 1-4 based upon the presence or absence of the marker, or thelevel of the marker.

The terms “statistical algorithm” or “statistical analysis” include alearning statistical classifier system. In some instances, the learningstatistical classifier system is selected from the group consisting of arandom forest, classification and regression tree, boosted tree, neuralnetwork, support vector machine, general chi-squared automaticinteraction detector model, interactive tree, multiadaptive regressionspline, machine learning classifier, and combinations thereof. Incertain instances, the statistical algorithm comprises a single learningstatistical classifier system. In other embodiments, the statisticalalgorithm comprises a combination of at least two learning statisticalclassifier systems. In some instances, the at least two learningstatistical classifier systems are applied in tandem. Non-limitingexamples of statistical algorithms and analysis suitable for use in theinvention are described in U.S. Patent Publication No. 2011/0045476, thedisclosure of which is hereby incorporated by reference in its entiretyfor all purposes.

The term “diagnostic model” includes a kynurenine score, mast cellscore, serotonin score, bile acid malabsorption score, microbiome score,inflammatory score, and combinations thereof. In a preferred aspect, aretrospective analysis is done on a cohort of known disease outcomeswith known complications and surgical procedures performed, as well ashealthy controls. In one aspect, a regression analysis (e.g., logisticregression) can be performed on the level of one or more kynureninemarkers, one or more mast cell markers, one or more serotonin markers,one or more bile acid malabsorption markers, one or more microbiomemarkers, and/or one or more inflammatory markers, to develop adiagnostic model.

III. Description of the Embodiments

A. Methods for Aiding in the Diagnosis of Irritable Bowel Syndrome

In one aspect, the present invention provides methods of aiding in thediagnosis of irritable bowel syndrome (IBS) in a subject.

FIG. 5 illustrates a flowchart for an exemplary embodiment of an IBSdiagnostic assay of the present invention. In certain embodiments, thediagnostic assay applies the measurements of celiac disease (CD) markersand computes a celiac disease score based on a first statisticalalgorithm for predicting CD vs. non-CD (105). The statistical modeldetermines if the patient has CD. If the celiac disease score comparedto a control score predicts that the patient is non-CD (110), the sampleproceeds to the next step of the method. This step applies themeasurements of inflammatory bowel disease (IBD) markers and computes anIBD score based on a second statistical algorithm for predicting IBD vs.non-IBD (120). If the patient's IBD score compared to a control scorepredicts that the patient is non-IBD (125), the sample proceeds to thenext step of the assay which is used to predict IBS from non-IBS (130).This step applies the combination of the patient's microbiome score(185), mast cell score (155), inflammatory score (195), bile acidmalabsorption score (175), oxidative stress score (145), and serotoninscore (165) that are based on measurements of bacterial antigen antibodymarkers (180), mast cell markers (150), inflammatory markers (190), bileacid malabsorption markers (170), kynurenine markers (140), andserotonin markers (160), respectively, to compute a disease score forpredicting IBS vs. non-IBS. If the patient's disease score is less thanthe cut-off, the algorithm predicts that the patient is non-IBS. If thepatient's disease score is greater than the cut-off, the patient ispredicted to have IBS.

In some embodiments, the method provided herein comprises: (a) measuringthe level of an array of celiac disease (CD) markers in a biologicalsample taken from the subject; (b) applying a statistical analysis tothe measured level of the array of CD markers to generate a CD score;(c) determining that the subject has CD based on the CD score comparedto that of a control cohort such as patients with CD.

In some embodiments, CD marker is selected from the group consisting ofan anti-gliadin IgA antibody, an anti-gliadin IgG antibody, ananti-tissue transglutaminase (tTG) antibody, an anti-endomysialantibody, and combinations thereof. In some embodiments, the statisticalanalysis transforms the level of the array of CD markers into an CDscore. In some embodiments, the CD score includes an empirically derivedprofile that is based upon an analysis of a plurality of CD markers. Inone aspect, the concentration of markers or their measured concentrationvalues are transformed into an index by an algorithm resident on acomputer. In certain aspects, the score is a synthetic or human derivedoutput, profile, or cut off value(s), which expresses the biologicaldata in numerical terms. The score can be used to determine or make oraid in making a clinical decision. In some embodiments, the statisticalanalysis includes applying a quartile analysis to the CD markers toabout obtain a quartile sum score (QSS) for the subject by convertingthe presence of level of the CD markers into a quartile score, andsumming the quartile score for each marker.

In some embodiments, the method comprises: (a) measuring the level of anarray of inflammatory bowel disease (IBD) markers in a biological sampletaken from the subject; (b) applying a statistical analysis to themeasured level of the array of IBD markers to generate a IBD score; and(c) determining that the subject has IBD based on the IBD score comparedto that of a control cohort such as patients with IBD.

In some embodiments, IBD marker is selected from the group consisting ofan anti-neutrophil cytoplasmic antibody (ANCA), an anti-Saccharomycescerevisiae immunoglobulin G (ASCA-IgA), an anti-Saccharomyces cerevisiaeimmunoglobulin G (ASCA-IgG), an anti-outer membrane protein C(anti-OmpC) antibody, an anti-flagellin antibody, a perinuclearanti-neutrophil cytoplasmic antibody (pANCA), an anti-Fla2 antibody, ananti-FlaX antibody, an anti-CBir antibody, ICAM-1, VCAM-1, VEGF, CRP,SAA, and combinations thereof. In some embodiments, the statisticalanalysis transforms the level of the array of IBD markers into an IBDscore. In some embodiments, the IBD score includes an empiricallyderived profile that is based upon an analysis of a plurality of IBDmarkers. In one aspect, the concentration of markers or their measuredconcentration values are transformed into an index by an algorithmresident on a computer. In certain aspects, the score is a synthetic orhuman derived output, profile, or cut off value(s), which expresses thebiological data in numerical terms. The score can be used to determineor make or aid in making a clinical decision. In some embodiments, thestatistical analysis includes applying a quartile analysis to the IBDmarkers to about obtain a quartile sum score (QSS) for the subject byconverting the presence of level of the IBD markers into a quartilescore, and summing the quartile score for each marker.

In some embodiments, the method comprises: (a) measuring the level of anarray of bacterial antigen antibody markers in a biological sample takenfrom the subject; and (b) applying a statistical analysis to themeasured level of the array of bacterial antigen antibody markers togenerate a bacterial antigen antibody marker score. In some embodiments,the bacterial antigen antibody marker is an antibody against a bacterialantigen, wherein the bacterial antigen is selected from the groupconsisting of Fla1, Fla2, FlaA, FliC, FliC2, FliC3, YBaN1, ECFliC,Ec0FliC, SeFljB, CjFlaA, CjFlaB, SJFliC, CjCgtA, Cjdmh, CjGT-A, EcYidX,EcEra, EcFrvX, EcGabT, EcYedK, EcYbaN, EcYhgN, RtMaga, RbCpaF, RgPilD,LaFrc, LaEno, LjEFTu, BjOmpa, PrOmpA, Cp10bA, CpSpA, EfSant, LmOsp,STET-2, Cpatox, Cpbtox, EcSta2, Ec0Stx2A, CjcdtB/C, CdtcdA/B, andcombinations thereof. In some embodiments, the statistical analysistransforms the level of the array of bacterial antigen antibody markersinto a microbiome score. In some embodiments, the microbiome scoreincludes an empirically derived profile that is based upon an analysisof a plurality of bacterial antigen antibody markers. In one aspect, theconcentration of markers or their measured concentration values aretransformed into an index by an algorithm resident on a computer. Incertain aspects, the score is a synthetic or human derived output,profile, or cut off value(s), which expresses the biological data innumerical terms. The score can be used to determine or make or aid inmaking a clinical decision. In some embodiments, the statisticalanalysis includes applying a quartile analysis to the bacterial antigenantibody markers to about obtain a quartile sum score (QSS) for thesubject by converting the presence of level of the bacterial antigenantibody markers into a quartile score, and summing the quartile scorefor each marker.

In some embodiments, the diagnostic model comprises a microbiome score.In some embodiments, the diagnostic model is established using aretrospective cohort with known outcomes of a clinical subtype of IBSand healthy controls. In some embodiments, the microbiome score isderived by applying logistic regression analysis to the level of one ormore bacterial antigen antibody markers determined in the retrospectivecohort.

In some embodiments, the method comprises: (a) measuring the level of anarray of mast cell markers in a biological sample taken from thesubject; and (b) applying a statistical analysis to the measured levelof the array of mast cell markers to generate a mast cell marker score.In some embodiments, the mast cell marker is selected from the groupconsisting of β-tryptase, histamine, prostaglandin E2, and combinationsthereof. In some embodiments, the statistical analysis transforms thelevel of the array of mast cell markers into a mast cell score. In someembodiments, the mast cell score includes an empirically derived profilethat is based upon an analysis of a plurality of mast cell markers. Inone aspect, the concentration of markers or their measured concentrationvalues are transformed into an index by an algorithm resident on acomputer. In certain aspects, the score is a synthetic or human derivedoutput, profile, or cut off value(s), which expresses the biologicaldata in numerical terms. The score can be used to determine or make oraid in making a clinical decision. A mast cell score can be measuredmultiple times over the course of time. In one aspect, the algorithm canbe trained with known samples and thereafter validated with samples ofknown identity. In some embodiments, the statistical analysis includesapplying a quartile analysis to the mast cell markers to about obtain aquartile sum score (QSS) for the subject by converting the presence oflevel of the mast cell markers into a quartile score, and summing thequartile score for each marker.

In some embodiments, the diagnostic model comprises a mast cell score.In some embodiments, the diagnostic model is established using aretrospective cohort with known outcomes of a clinical subtype of IBSand healthy controls. In some embodiments, the mast cell score isderived by applying logistic regression analysis to the level of one ormore mast cell markers determined in the retrospective cohort.

In some embodiments, the method comprises: (a) measuring the level of anarray of inflammatory markers in a biological sample taken from thesubject; and (b) applying a statistical analysis to the measured levelof the array of inflammatory markers to generate a inflammatory score.In some embodiments, inflammatory marker is selected from the groupconsisting of BDNF, EGF, VEGF, amphiregulin, NGAL, TWEAK, GRO-α, IL-1β,IL-8, TIMP1, CRP, SAA, ICAM-1, VCAM-1, and combinations thereof. In someembodiments, the statistical analysis transforms the level of the arrayof inflammatory markers into an inflammatory score. In some embodiments,the inflammatory score includes an empirically derived profile that isbased upon an analysis of a plurality of inflammatory markers. In oneaspect, the concentration of markers or their measured concentrationvalues are transformed into an index by an algorithm resident on acomputer. In certain aspects, the score is a synthetic or human derivedoutput, profile, or cut off value(s), which expresses the biologicaldata in numerical terms. The score can be used to determine or make oraid in making a clinical decision. An inflammatory score can be measuredmultiple times over the course of time. In one aspect, the algorithm canbe trained with known samples and thereafter validated with samples ofknown identity. In some embodiments, the statistical analysis includesapplying a quartile analysis to the inflammatory markers to about obtaina quartile sum score (QSS) for the subject by converting the presence oflevel of the inflammatory markers into a quartile score, and summing thequartile score for each marker.

In some embodiments, the diagnostic model comprises an inflammatoryscore. In some embodiments, the diagnostic model is established using aretrospective cohort with known outcomes of a clinical subtype of IBSand healthy controls. In some embodiments, the inflammatory score isderived by applying logistic regression analysis to the level of one ormore inflammatory markers determined in the retrospective cohort.

In some embodiments, the method comprises: (a) measuring the level of anarray of kynurenine markers in a biological sample taken from thesubject; and (b) applying a statistical analysis to the measured levelof the array of kynurenine markers to generate a kynurenine score (e.g.,oxidative stress score). In some embodiments, kynurenine marker isselected from the group consisting of kynurenine (K), kynurenic acid(KyA), anthranilic acid (AA), 3-hydroxykynurenine (3-HK),3-hydroxyanthranilic acid (3-HAA), xanthurenic acid (XA), quinolinicacid (QA), tryptophan, 5-hydroxytryptophan (5-HTP), and combinationsthereof. In some embodiments, the statistical analysis transforms thelevel of the array of kynurenine markers into a kynurenine score. Insome embodiments, the kynurenine score includes an empirically derivedprofile that is based upon an analysis of a plurality of kynureninemarkers. In one aspect, the concentration of markers or their measuredconcentration values are transformed into an index by an algorithmresident on a computer. In certain aspects, the score is a synthetic orhuman derived output, profile, or cut off value(s), which expresses thebiological data in numerical terms. The score can be used to determineor make or aid in making a clinical decision. A kynurenine score can bemeasured multiple times over the course of time. In one aspect, thealgorithm can be trained with known samples and thereafter validatedwith samples of known identity. In some embodiments, the statisticalanalysis includes applying a quartile analysis to the kynurenine markersto about obtain a quartile sum score (QSS) for the subject by convertingthe presence of level of the kynurenine markers into a quartile score,and summing the quartile score for each marker.

In some embodiments, the diagnostic model comprises a kynurenine score.In some embodiments, the diagnostic model is established using aretrospective cohort with known outcomes of a clinical subtype of IBSand healthy controls. In some embodiments, the kynurenine score isderived by applying logistic regression analysis to the level of one ormore kynurenine markers determined in the retrospective cohort.

In some embodiments, the method comprises: (a) measuring the level of anarray of serotonin markers in a biological sample taken from thesubject; and (b) applying a statistical analysis to the measured levelof the array of serotonin markers to generate a serotonin score. In someembodiments, serotonin marker is selected from the group consisting ofserotonin (5-HT), 5-hydroxyindoleacetic acid (5-HIAA),serotonin-O-sulfate, serotonin-O-phosphate and combinations thereof. Insome embodiments, the statistical analysis transforms the level of thearray of serotonin markers into a serotonin score. In some embodiments,the serotonin score includes an empirically derived profile that isbased upon an analysis of a plurality of serotonin markers. In oneaspect, the concentration of markers or their measured concentrationvalues are transformed into an index by an algorithm resident on acomputer. In certain aspects, the score is a synthetic or human derivedoutput, profile, or cut off value(s), which expresses the biologicaldata in numerical terms. The score can be used to determine or make oraid in making a clinical decision. A serotonin score can be measuredmultiple times over the course of time. In one aspect, the algorithm canbe trained with known samples and thereafter validated with samples ofknown identity. In some embodiments, the statistical analysis includesapplying a quartile analysis to the serotonin markers to about obtain aquartile sum score (QSS) for the subject by converting the presence oflevel of the serotonin markers into a quartile score, and summing thequartile score for each marker.

In some embodiments, the diagnostic model comprises a serotonin score.In some embodiments, the diagnostic model is established using aretrospective cohort with known outcomes of a clinical subtype of IBSand healthy controls. In some embodiments, the serotonin score isderived by applying logistic regression analysis to the level of one ormore serotonin markers determined in the retrospective cohort.

In some embodiments, the method comprises: (a) measuring the level of anarray of bile acid malabsorption (BAM) markers in a biological sampletaken from the subject; and (b) applying a statistical analysis to themeasured level of the array of BAM markers to generate a BAM score. Insome embodiments, BAM marker is selected from the group consisting of7α-hydroxy-4-cholesten-3-one, FGF19, and combinations thereof. In someembodiments, the statistical analysis transforms the level of the arrayof BAM markers into a serotonin score. In some embodiments, the BAMscore includes an empirically derived profile that is based upon ananalysis of a plurality of BAM markers. In one aspect, the concentrationof markers or their measured concentration values are transformed intoan index by an algorithm resident on a computer. In certain aspects, thescore is a synthetic or human derived output, profile, or cut offvalue(s), which expresses the biological data in numerical terms. Thescore can be used to determine or make or aid in making a clinicaldecision. A BAM score can be measured multiple times over the course oftime. In one aspect, the algorithm can be trained with known samples andthereafter validated with samples of known identity. In someembodiments, the statistical analysis includes applying a quartileanalysis to the BAM markers to about obtain a quartile sum score (QSS)for the subject by converting the presence of level of the BAM markersinto a quartile score, and summing the quartile score for each marker.

In some embodiments, the diagnostic model comprises a BAM score. In someembodiments, the diagnostic model is established using a retrospectivecohort with known outcomes of a clinical subtype of IBS and healthycontrols. In some embodiments, the BAM score is derived by applyinglogistic regression analysis to the level of one or more BAM markersdetermined in the retrospective cohort.

In some embodiments, a disease score is generated for the subject byusing an algorithm that integrates the subject's microbiome score, mastcell score, inflammatory score, BAM score, kynurenine score andserotonin score. The subject's disease score can be compared to adiagnostic model to determine whether the subject has an increasedlikelihood of having IBS compared to being a healthy control.

In some embodiments, the diagnostic model is based on a combination ofthe microbiome score, mast cell score, inflammatory score, bile acidmalabsorption score, kynurenine score, and serotonin score from aretrospective cohort of patients with known IBS outcomes and healthycontrols. For instance, the diagnostic model can represent the diseasescores for a retrospective cohort of patients with known IBS outcomesand healthy controls. In some embodiments, the diagnostic modelcomprises a logistic regression model.

In some embodiments, the diagnostic model comprises a IBS diagnosticcut-off value wherein a disease score that is higher than the cut-offvalue indicates that the subject has IBS and/or a subtype of IBS. Inother instances, a disease score that is lower than the cut-off valuecan indicate that the subject does not have IBS.

The sample used for detecting or determining the level of at least onebiomarker is typically whole blood, plasma, serum, saliva, urine, stool(i.e., feces), tears, and any other bodily fluid, or a tissue sample(i.e., biopsy) such as a small intestine or colon sample. Preferably,the sample is serum, whole blood, plasma, stool, urine, or a tissuebiopsy. In certain instances, the methods of the present inventionfurther comprise obtaining the sample from the individual prior todetecting or determining the level of at least one biomarker in thesample. In a preferred embodiment, the additional biomarker is detectedfrom a blood or serum sample. In other embodiments, the biomarker isdetected from a saliva sample, a urine sample, a stool sample or abiopsy from the bowel of the subject.

B. Bacterial Antigen Antibody Markers (e.g., Microbiome Markers)

As used herein, the term “bacterial antigen antibody” refers to anantibody that specifically binds to a bacterial antigen or an antigenicfragment thereof, such as an anti-bacterial antigen antibody. Withoutbeing bound to any particular theory, individuals with IBS or otherdisorders involving the gastrointestinal microbiota can developanti-bacterial antigen antibodies.

In one aspect, the present invention provides methods for aiding in thediagnosis of IBS and/or subtypes of IBS by detecting the level of atleast one bacterial antigen antibody marker in a sample. The bacterialantigen antibody marker includes antibodies that specifically bind to abacterial antigen including, but not limited to, Fla1, Fla2, FlaA, FliC,FliC2, FliC3, YBaN1, ECFliC, Ec0FliC, SeFljB, CjFlaA, CjFlaB, SfFliC,CjCgtA, Cjdmh, CjGT-A, EcYidX, EcEra, EcFrvX, EcGabT, EcYedK, EcYbaN,EcYhgN, RtMaga, RbCpaF, RgPilD, LaFrc, LaEno, LjEFTu, BjOmpa, PrOmpA,Cp10bA, CpSpA, EfSant, LmOsp, STET-2, Cpatox, Cpbtox, EcSta2, Ec0Stx2A,CjcdtB/C, CdtcdA/B, and a combination thereof.

In some embodiments, the method comprises measuring the level of atleast one bacterial antigen antibody markers in a biological sampletaken from the subject. In some instance, any 1-tuple, 2-tuple, 3-tuple,4-tuple, 5-tuple, 6-tuple, 7-tuple, 8-tuple, 9-tuple, 10-tuple,11-tuple, 12-tuple, 13-tuple, 15-tuple 16-tuple, 17-tuple, 18-tuple,19-tuple, 20-tuple, 21-tuple, 22-tuple, 23-tuple, 24-tuple, 25-tuple,26-tuple, 27-tuple, 28-tuple, 29-tuple, 30-tuple, 31-tuple, 32-tuple,33-tuple, 34-tuple or 35-tuple for the bacterial antigen antibodies canbe measured.

In some embodiments, the level of at least one bacterial antigenantibody marker, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,33, 34, 35 or more bacterial antigen antibody markers are increased inan individual with IBS compared to a healthy control. In otherembodiments, the level of at least one bacterial antigen antibodymarker, e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35or more bacterial antigen antibody markers are decreased in anindividual with IBS compared to a healthy control. In some embodiments,the level of an array of bacterial antigen antibody markers isdysregulated in a sample taken from an individual with IBS compared toone from a healthy control.

In some embodiments, the method comprises: a) contacting a biologicalsample from the subject with a bacterial antigen polypeptide or anantigenic fragment thereof under conditions suitable to transform thebacterial antigen antibody present in the sample into a complexcomprising the bacterial antigen antibody and the bacterial antigenpolypeptide or fragment thereof; and (b) determining the level of thecomplex, thereby determining the level of the bacterial antigen presentin the sample. In some embodiments, the method further comprises: (c)comparing the level of the bacterial antigen antibody present in thesample to a control level of the bacterial antigen antibody, wherein thelevel of the bacterial antigen antibody is indicative of an increasedlikelihood of the subject having IBS.

The bacterial antigen polypeptide or fragment thereof selectively bindsto the bacterial antigen antibody to be measured. For example, the levelof an antibody against bacteria flagellin (e.g., SJFliC) can be measuredusing the flagellin polypeptide or an antigenic fragment thereof.

In a specific embodiment, the invention provides a method to aid in thediagnosis of IBS, the method comprises: (a) contacting a sample having abacterial antigen antibody contained therein under conditions suitableto transform the bacterial antigen antibody into a complex comprisingthe bacterial antigen and the captured anti-bacterial antigen antibody;(b) contacting the complex with an enzyme labeled indicator antibody totransform the complex into a labeled complex; (c) contacting the labeledcomplex with a substrate for the enzyme; and (d) detecting the presenceor level of the bacterial antigen antibody in the sample.

In certain other embodiments, the level of at least one bacterialantigen antibody marker is determined using an immunoassay (e.g., ELISA)or an immunohistochemical assay. A non-limiting example of animmunoassay suitable for use in the methods of the present inventionincludes an enzyme-linked immunosorbent assay (ELISA). Examples ofimmunohistochemical assays suitable for use in the methods of thepresent invention include, but are not limited to, immunofluorescenceassays such as direct fluorescent antibody assays, indirect fluorescentantibody (IFA) assays, anticomplement immunofluorescence assays, andavidin-biotin immunofluorescence assays. Other types ofimmunohistochemical assays include immunoperoxidase assays. SuitableELISA kits for determining the presence of level of a bacterial antigenin a serum, plasma, saliva, or urine sample, are available from e.g.,Antigenix America Inc. (Huntington station, NY), Promega (Madison,Wis.), R&D Systems, Inc. (Minneapolis, Minn.), Life Technologies(Carlsbad, Calif.), CHEMICON International, Inc. (Temecula, Calif.),Neogen Corp. (Lexington, Ky.), PeproTech (Rocky Hill, N.J.), AlpcoDiagnostics (Salem, N.H.), Pierce Biotechnology, Inc. (Rockford, Ill.),and/or Abazyme (Needham, Mass.).

In one aspect, the present invention provides an assay for the detectionof a bacterial antigen antibody marker in a sample, the methodcomprising the steps of: (a) coating a solid phase surface with abacterial antigen or antigenic fragment thereof; (b) contacting thesolid phase surface with a sample under conditions suitable to transformthe bacterial antigen antibody present in the sample into a complexcomprising the bacterial antigen and the bacterial antigen antibody; (c)contacting the bacterial antigen and the bacterial antigen antibody witha detecting antibody under conditions suitable to form a ternarycomplex; and (d) contacting the ternary complex with a luminescent orchemiluminescent substrate.

In one embodiment, the detecting antibody is conjugated to alkalinephosphatase. In other embodiments, the detecting antibody is notconjugated to an enzyme and the method further comprises the steps of(i) contacting the ternary complex with a third antibody conjugated toalkaline phosphatase under conditions suitable to form a quaternarycomplex and (ii) contacting the quaternary complex with a luminescent orchemiluminescent substrate.

Any suitable antibody pair may be used for the capture and detectingantibodies in a sandwich ELISA. One of skill in the art will know andappreciate how to select an appropriate antibody pair for the assay.Generally, two antibodies are selected that bind to the target ofinterest, e.g., β-tryptase, at different epitopes such that the bindingof the first (capture) antibody does not interfere with the second(detecting) antibody. In certain embodiments, the detecting antibodywill be conjugated to an enzyme, for example, alkaline phosphatase, toaid in the detection of the complex. In other embodiments, a secondaryantibody conjugated to an enzyme (e.g., alkaline phosphatase), whichbinds to the detecting antibody, may be used in the assay.

Generally, the complex will be detected by the use of a luminescentsubstrate, for example, a luminescent substrate found in a kit such asUltra LITE (NAG Research Laboratories); SensoLyte (AnaSpec); SuperSignalELISA Femto Maximum Sensitivity Substrate (Thermo Scientific);SuperSignal ELISA Pico Chemiluminescent Substrate (Thermo Scientific);or CPSD (disodium3-(4-methoxyspiro{1,2-dioxetane-3,2′-(5′-chloro)tricyclo[3.3.1.13,7]decan}-4-yl)phenylphosphate; Tropix, Inc).

The amino acid sequence of an antigenic fragment of a bacterial antigencan be identified by predicting the immunogenic sites in silico usingsoftware algorithms such as EMBOSS. For instance, the hydrophilicity,accessibility and flexibility properties of a series of peptidefragments of an antigen protein are accessed to determine the peptidefragments that are predicted to be the most antigenic (e.g., have thehighest antigenic score).

In certain embodiments, a variety of bacterial antigens are particularlyuseful in the methods of the present invention for aiding in thediagnosis of IBS. Non-limiting examples of bacterial antigens includeflagellin polypeptides or fragments thereof, and other polypeptides orfragments thereof that are expressed by the gastrointestinal microbiota.Microbial flagellin is a protein found in bacterial flagellum thatarrange itself in a hollow cylinder to form the filament. Flagellinpolypeptides or fragments thereof are typically expressed by bacteriaincluding Clostridium, Lachnospiraceae bacterium A4, E. coli K12, E.coli O157:H7, Shigella flexneri, Campylobacter jejuni, and Salmonellaenteritidis.

The presence of anti-flagellin antibody in a sample from an individualcan be determined using a flagellin protein or a fragment thereof suchas an immunoreactive fragment thereof. Suitable flagellin antigensuseful in determining anti-flagellin antibody levels in a sampleinclude, without limitation, a flagellin protein such as CBir-1, FliC,FljB, flagellin, flagellin X (Fla-X), flagellin A (FlaA), flagellin B(FlaB), flagellin 2 (Fla2), fragments thereof, and combinations thereof,a flagellin polypeptide having substantially the same amino acidsequence as the flagellin protein, or a fragment thereof such as animmunoreactive fragment thereof. As used herein, a flagellin polypeptidegenerally describes polypeptides having an amino acid sequence withgreater than about 50% identity, preferably greater than about 60%identity, more preferably greater than about 70% identity, still morepreferably greater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99%amino acid sequence identity with a naturally-occurring flagellinprotein, with the amino acid identity determined using a sequencealignment program such as CLUSTALW. Such flagellin antigens can beprepared, e.g., by purification from bacterium such as HelicobacterBilis, Helicobacter mustelae, Helicobacter pylori, Lachnospiraceaebacterium A4, Shigella flexneri, Escherichia coli, Salmonellaenteritidis, Campylobacter jejuni, Butyrivibrio fibrisolvens, andbacterium found in the cecum, by recombinant expression of a nucleicacid encoding a flagellin antigen, by synthetic means such as solutionor solid phase peptide synthesis.

Non-limiting examples of bacterial antigens are presented in Table 1.

TABLE 1 Bacteria antigens Antigen Grouping Phyla Strain UniProt EcFliCInfectious Proteobacteria E. coli P04949 EcOFliC InfectiousProteobacteria E. coli H7:0157 Q7AD06 SeFljB Infectious ProteobacteriaS. enterotidis B5R0Z9 CjFlaA Infectious Proteobacteria C. jejuni Q2M5R2CjFlaB Infectious Proteobacteria C. jejuni A1W0V5 SfFliC InfectiousProteobacteria S. flexneri Q08860 CjCgtA Infectious Proteobacteria C.jejuni Q50FZ3 Cjdmh Infectious Proteobacteria C. jejuni Q50FQ7 CjGT-AInfectious Proteobacteria C. jejuni Q50FX0 EcYidX CommensalProteobacteria E. coli P0ADM6 EcEra Commensal Proteobacteria E. coliU6NG20 EcFrvX Commensal Proteobacteria E. coli P32153 EcGabT CommensalProteobacteria E. coli P22256 EcYedK Commensal Proteobacteria E. coliP76318 EcYbaN Commensal Proteobacteria E. coli P0AAR5 EcYhgN CommensalProteobacteria E. coli P67143 RtMaga Mucin-degr. Firmicutes R. torquesD4M4S6 RbCpaF Mucin-degr. Firmicutes R. bromii D4L5L7 RgPilD Mucin-degr.Firmicutes R. gnavus A7B5T4 LaFrc Commensal Firmicutes L. acidophilusR4JZC5 LaEno Commensal Firmicutes L. acidophilus Q5FKM6 LjEFTu CommensalFirmicutes L. johnsonii Q74JU6 BfOmpA Commensal Bacteriodetes B.fragilis Q64VP7 PrOmpA Commensal Bacteriodetes Prevotella spp. C9PT48Cp10bA Commensal Firmicutes C. perfringens B1V1I2 CpSpA CommensalFirmicutes C. perfringens Q5DWA9 EfSant Commensal Firmicutes E. faecalisC7W575 LmOsp Commensal Firmicutes L. monocytogenes B8DFK3 SfET-2 ToxinsProteobacteria S. flexneri Q7BEN0 Cpatox Toxins Firmicutes C.perfringens Q3HR45 Cpbtox Toxins Firmicutes C. perfringens B1R976 EcSta2Toxins Proteobacteria E. coli Q2WE95 Ec0Stx2A Toxins Proteobacteria E.coli H7:0157 B6ZXF5 CjcdtB/C Toxins Proteobacteria C. jejuni Q46101/Q46102 CdtcdA/B Toxins Firmicutes C. difficile P16154/ P18177

The term “EcFliC” refers to a flagellin of Escherichia coli strain K12that is immunoreactive with an anti-FliC antibody. Suitable EcFliCantigens useful in determining anti-FliC antibody levels in a sampleinclude, without limitation, a FliC protein of Escherichia coli strainK12, a FliC polypeptide having substantially the same amino acidsequence as the FliC protein of Escherichia coli strain K12, or afragment thereof such as an immunoreactive fragment thereof. A FliCpolypeptide of Escherichia coli strain K12 generally describespolypeptides having an amino acid sequence with greater than about 50%identity, preferably greater than about 60% identity, more preferablygreater than about 70% identity, still more preferably greater thanabout 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acid sequenceidentity with a FliC protein of Escherichia coli strain K12, with theamino acid identity determined using a sequence alignment program suchas CLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such as E. coli, by recombinant expression of anucleic acid encoding a FliC peptide such as NCBI Accession No.AAA23950.1, by synthetic means such as solution or solid phase peptidesynthesis.

The term “EcOFliC” refers to a flagellin of Escherichia coli strainO157:H7 that is immunoreactive with an anti-FliC antibody. Suitable FliCantigens useful in determining anti-FliC antibody levels in a sampleinclude, without limitation, a FliC protein, a FliC polypeptide havingsubstantially the same amino acid sequence as the FliC protein, or afragment thereof such as an immunoreactive fragment thereof. A FliCpolypeptide generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with a FliC protein, with theamino acid identity determined using a sequence alignment program suchas CLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such as E. coli strain O157:H7, by recombinantexpression of a nucleic acid encoding a FliC peptide such as NCBIAccession No. BAB36085.1, by synthetic means such as solution or solidphase peptide synthesis.

The term “SeFljB” refers to a flagellin protein of Salmonellaenteritidis that is immunoreactive with an anti-Fljb antibody. SuitableSeFljB antigens useful in determining anti-Fljb antibody levels in asample include, without limitation, a Fljb protein, a Fljb polypeptidehaving substantially the same amino acid sequence as the Fljb protein,or a fragment thereof such as an immunoreactive fragment thereof. A Fljbpolypeptide generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with a Fljb protein, with theamino acid identity determined using a sequence alignment program suchas CLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such as S. enteritidis, by recombinant expressionof a nucleic acid encoding a Fljb peptide such as Uniprot No. B5R0Z9, bysynthetic means such as solution or solid phase peptide synthesis.

The term “CjFlaA” refers to a flagellin subunit of the Campylobacterjejuni that is immunoreactive with an anti-FlaA antibody. SuitableCjFlaA antigens useful in determining anti-FlaA antibody levels in asample include, without limitation, a FlaA protein, a FlaA polypeptidehaving substantially the same amino acid sequence as the FlaA protein,or a fragment thereof such as an immunoreactive fragment thereof. A FlaApolypeptide generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with a FlaA protein, with theamino acid identity determined using a sequence alignment program suchas CLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such as Campylobacter jejuni, by recombinantexpression of a nucleic acid encoding a FlaA peptide such as NCBIAccession No. ABC69276.1, by synthetic means such as solution or solidphase peptide synthesis.

The term “CjFlaB” refers to a flagellin B of the Campylobacter jejunithat is immunoreactive with an anti-FlaB antibody. Suitable CjFlaBantigens useful in determining anti-FlaB antibody levels in a sampleinclude, without limitation, a FlaB protein, a FlaB polypeptide havingsubstantially the same amino acid sequence as the FlaB protein, or afragment thereof such as an immunoreactive fragment thereof. A FlaBpolypeptide generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with a FlaB protein, with theamino acid identity determined using a sequence alignment program suchas CLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such as Campylobacter jejuni, by recombinantexpression of a nucleic acid encoding a FlaB peptide such as NCBIAccession EAQ72883.1, by synthetic means such as solution or solid phasepeptide synthesis.

The term “SJFliC” refers to a flagellin of Shigella flexneri that isimmunoreactive with an anti-FliC antibody. Suitable SJFliC antigensuseful in determining anti-FliC antibody levels in a sample include,without limitation, a FliC protein, a FliC polypeptide havingsubstantially the same amino acid sequence as the FliC protein, or afragment thereof such as an immunoreactive fragment thereof. A FliCpolypeptide generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with a FliC protein, with theamino acid identity determined using a sequence alignment program suchas CLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such as Shigella flexneri, by recombinantexpression of a nucleic acid encoding a FliC peptide such as NCBIAccession No. BAA04093.1, by synthetic means such as solution or solidphase peptide synthesis. One skilled in the art will appreciate thatShigella flexneri FliC is also known as flagellar filament structuralprotein, flagellin, and H-antigen.

The term “Cj81-045” or “CjGT-A” refers to a Campylobacter jejunimembrane protein that is immunoreactive with an anti-Cj81-045 (CjGT-A)antibody. Suitable Cj81-045 (CjGT-A) antigens useful in determininganti-Cj81-045 (-CjGT-A) antibody levels in a sample include, withoutlimitation, a Cj81-045 (CjGT-A) protein, a Cj81-045 (CjGT-A) polypeptidehaving substantially the same amino acid sequence as the Cj81-045(CjGT-A) protein, or a fragment thereof such as an immunoreactivefragment thereof. A Cj81-045 (CjGT-A) polypeptide generally describespolypeptides having an amino acid sequence with greater than about 50%identity, preferably greater than about 60% identity, more preferablygreater than about 70% identity, still more preferably greater thanabout 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acid sequenceidentity with a Cj81-045 (CjGT-A) protein, with the amino acid identitydetermined using a sequence alignment program such as CLUSTALW. Suchantigens can be prepared, for example, by purification from entericbacteria such as Campylobacter jejuni, by recombinant expression of anucleic acid encoding a Cj81-045 (CjGT-A) peptide such as NCBI AccessionAAW56124.1, by synthetic means such as solution or solid phase peptidesynthesis.

The term “Cj81-128” or “Cjdmh” refers to a Campylobacter jejuni membraneprotein that is immunoreactive with an anti-Cj81-128 (-Cjdmh) antibody.Suitable Cj81-128 (Cjdmh) antigens useful in determining anti-Cj81-128(-Cjdmh) antibody levels in a sample include, without limitation, aCj81-128 (Cjdmh) protein, a Cj81-128 (Cjdmh) polypeptide havingsubstantially the same amino acid sequence as the Cj81-128 (Cjdmh)protein, or a fragment thereof such as an immunoreactive fragmentthereof. A Cj81-128 (Cjdmh) polypeptide generally describes polypeptideshaving an amino acid sequence with greater than about 50% identity,preferably greater than about 60% identity, more preferably greater thanabout 70% identity, still more preferably greater than about 80%, 85%,90%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity with aCj81-128 (Cjdmh) protein, with the amino acid identity determined usinga sequence alignment program such as CLUSTALW. Such antigens can beprepared, for example, by purification from enteric bacteria such asCampylobacter jejuni, by recombinant expression of a nucleic acidencoding a Cj81-128 (Cjdmh) peptide such as NCBI Accession AAW56187.1,by synthetic means such as solution or solid phase peptide synthesis.

The term “Cj81-008” or “CjCgtA” refers to abeta-1,4-N-acetylgalactosaminyltransferase of Campylobacter jejuni thatis immunoreactive with an anti-Cj81-008 (-CjCgtA) antibody. SuitableCj81-008 (CjCgtA) antigens useful in determining anti-Cj81-008 (-CjCgtA)antibody levels in a sample include, without limitation, a Cj81-008(CjCgtA) protein, a Cj81-008 (CjCgtA) polypeptide having substantiallythe same amino acid sequence as the Cj81-008 (CjCgtA) protein, or afragment thereof such as an immunoreactive fragment thereof. A Cj81-008(CjCgtA) polypeptide generally describes polypeptides having an aminoacid sequence with greater than about 50% identity, preferably greaterthan about 60% identity, more preferably greater than about 70%identity, still more preferably greater than about 80%, 85%, 90%, 95%,96%, 97%, 98%, or 99% amino acid sequence identity with a Cj81-008(CjCgtA) protein, with the amino acid identity determined using asequence alignment program such as CLUSTALW. Such antigens can beprepared, for example, by purification from enteric bacteria such asCampylobacter jejuni, by recombinant expression of a nucleic acidencoding a Cj81-008 (CjCgtA) peptide such as NCBI Accession AAW56101.1,by synthetic means such as solution or solid phase peptide synthesis.

The term “EcYidX” refers to a putative replicase of the Escherichia colistrain K12 that is immunoreactive with an anti-YidX antibody. YidX ispredicted to be a lipoprotein C. Suitable YidX antigens useful indetermining anti-YidX antibody levels in a sample include, withoutlimitation, a YidX protein of the E. coli strain K12, a YidX polypeptidehaving substantially the same amino acid sequence as the YidX protein,or a fragment thereof such as an immunoreactive fragment thereof. A YidXpolypeptide generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with a YidX protein of the E.coli strain K12, with the amino acid identity determined using asequence alignment program such as CLUSTALW. Such antigens can beprepared, for example, by purification from enteric bacteria such as E.coli, by recombinant expression of a nucleic acid encoding a YidXpeptide such as NCBI Accession No. AAT48200.1, by synthetic means suchas solution or solid phase peptide synthesis. One skilled in the artwill appreciate that YidX is also known as predicted lipoprotein C.

The term “EcEra” refers to a Ras-like membrane-associated,ribosome-binding GTPase of the Escherichia coli strain K12 that isimmunoreactive with an anti-Era antibody. Suitable EcEra antigens usefulin determining anti-Era antibody levels in a sample include, withoutlimitation, an Era protein of the Escherichia coli strain K12, an Erapolypeptide having substantially the same amino acid sequence as the Eraprotein of the E. coli strain K12, or a fragment thereof such as animmunoreactive fragment thereof. An Era polypeptide of the E. colistrain K12 generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with an Era protein of the E.coli strain K12, with the amino acid identity determined using asequence alignment program such as CLUSTALW. Such antigens can beprepared, for example, by purification from enteric bacteria such as E.coli strain K12, by recombinant expression of a nucleic acid encoding anEra peptide such as NCBI Accession No. AAA03242.1, by synthetic meanssuch as solution or solid phase peptide synthesis. One skilled in theart will appreciate that Era is also known as membrane-associated 16SrRNA-binding GTPase, B2566, SdgE and RbaA.

The term “EcFrvX” refers to a fry operon protein of the Escherichia colistrain K12 that is immunoreactive with an anti-FrvX antibody. FrvX ispredicted to be an endo-1,4-beta-glucanase. Suitable EcFrvX antigensuseful in determining anti-FrvX antibody levels in a sample include,without limitation, a FrvX protein of the E. coli strain K12, a FrvXpolypeptide having substantially the same amino acid sequence as theFrvX protein of the E. coli strain K12, or a fragment thereof such as animmunoreactive fragment thereof. A FrvX polypeptide of the E. coli trainK12 generally describes polypeptides having an amino acid sequence withgreater than about 50% identity, preferably greater than about 60%identity, more preferably greater than about 70% identity, still morepreferably greater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99%amino acid sequence identity with a FrvX protein of the E. coli strainK12, with the amino acid identity determined using a sequence alignmentprogram such as CLUSTALW. Such antigens can be prepared, for example, bypurification from enteric bacteria such as E. coli, by recombinantexpression of a nucleic acid encoding a FrvX peptide such as NCBIAccession No. AAB03031.1, by synthetic means such as solution or solidphase peptide synthesis.

The term “EcGabT” refers to a PLP-dependent 4-aminobutyrateaminotransferase of the Escherichia coli strain K12 that isimmunoreactive with an anti-GabT antibody. Suitable EcGabT antigensuseful in determining anti-GabT antibody levels in a sample include,without limitation, a GabT protein of the E. coli strain K12, a GabTpolypeptide having substantially the same amino acid sequence as theGabT protein of the Escherichia coli strain K12, or a fragment thereofsuch as an immunoreactive fragment thereof. A GabT polypeptide generallydescribes polypeptides having an amino acid sequence with greater thanabout 50% identity, preferably greater than about 60% identity, morepreferably greater than about 70% identity, still more preferablygreater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acidsequence identity with a GabT protein of the E. coli strain K12, withthe amino acid identity determined using a sequence alignment programsuch as CLUSTALW. Such antigens can be prepared, for example, bypurification from enteric bacteria such as E. coli, by recombinantexpression of a nucleic acid encoding a GabT peptide such as NCBIAccession No. AAC36832.1, by synthetic means such as solution or solidphase peptide synthesis, or by using phage display. One skilled in theart will appreciate that GabT is also known as(S)-3-amino-2-methylpropionate transaminase, GABA aminotransferase,GABA-AT, Gamma-amino-N-butyrate transaminase, and glutamate:succinicsemialdehyde transaminase L-AIBAT.

The term “EcYedK” refers to a Escherichia coli strain K12 predictedprotein that is that is immunoreactive with an anti-YedK antibody.Suitable EcYedK antigens useful in determining anti-YedK antibody levelsin a sample include, without limitation, a YedK protein of the E. colistrain K12, a YedK polypeptide having substantially the same amino acidsequence as the YedK protein of the E. coli strain K12, or a fragmentthereof such as an immunoreactive fragment thereof. A YedK polypeptidegenerally describes polypeptides having an amino acid sequence withgreater than about 50% identity, preferably greater than about 60%identity, more preferably greater than about 70% identity, still morepreferably greater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99%amino acid sequence identity with a YedK protein of the E. coli strainK12, with the amino acid identity determined using a sequence alignmentprogram such as CLUSTALW. Such antigens can be prepared, for example, bypurification from enteric bacteria such as E. coli, by recombinantexpression of a nucleic acid encoding a YedK peptide such as NCBIAccession No. AA48139, by synthetic means such as solution or solidphase peptide synthesis, or by using phage display.

The term “EcYbaN” refers to a Escherichia coli strain K12 inner membraneprotein YbaN and that is immunoreactive with an anti-YbaN antibody.Suitable EcYbaN antigens useful in determining anti-YbaN antibody levelsin a sample include, without limitation, a YbaN protein of the E. colistrain K12, a YbaN polypeptide having substantially the same amino acidsequence as the YbaN protein of the E. coli strain K12, or a fragmentthereof such as an immunoreactive fragment thereof. A YbaN polypeptidegenerally describes polypeptides having an amino acid sequence withgreater than about 50% identity, preferably greater than about 60%identity, more preferably greater than about 70% identity, still morepreferably greater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99%amino acid sequence identity with a YbaN protein of the E. coli strainK12, with the amino acid identity determined using a sequence alignmentprogram such as CLUSTALW. Such antigens can be prepared, for example, bypurification from enteric bacteria such as E. coli, by recombinantexpression of a nucleic acid encoding a YbaN peptide such as Uniprot No.P0AAR5, by synthetic means such as solution or solid phase peptidesynthesis, or by using phage display.

The term “EcYhgN” refers to a Escherichia coli strain K12 membraneprotein that is predicted to function as an antibiotic transporter andthat is immunoreactive with an anti-YhgN antibody. Suitable EcYhgNantigens useful in determining anti-YhgN antibody levels in a sampleinclude, without limitation, a YhgN protein of the E. coli strain K12, aYhgN polypeptide having substantially the same amino acid sequence asthe YhgN protein of the E. coli strain K12, or a fragment thereof suchas an immunoreactive fragment thereof. A YhgN polypeptide generallydescribes polypeptides having an amino acid sequence with greater thanabout 50% identity, preferably greater than about 60% identity, morepreferably greater than about 70% identity, still more preferablygreater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acidsequence identity with a YhgN protein of the E. coli strain K12, withthe amino acid identity determined using a sequence alignment programsuch as CLUSTALW. Such antigens can be prepared, for example, bypurification from enteric bacteria such as E. coli, by recombinantexpression of a nucleic acid encoding a YhgN peptide such as NCBIAccession No. AAA58232.1, by synthetic means such as solution or solidphase peptide synthesis, or by using phage display. One skilled in theart will appreciate that YhgN is also known as predicted antibiotictransporter.

The term “RtMaga” refers to a Ruminococcus torques mannosyl-glycoproteinendo-beta-N-acetylglucosaminidase and that is immunoreactive with ananti-Maga antibody. Suitable RtMaga antigens useful in determininganti-Maga antibody levels in a sample include, without limitation, aMaga protein of the Ruminococcus torques, a Maga polypeptide havingsubstantially the same amino acid sequence as the Maga protein of theRuminococcus torques, or a fragment thereof such as an immunoreactivefragment thereof. A Maga polypeptide generally describes polypeptideshaving an amino acid sequence with greater than about 50% identity,preferably greater than about 60% identity, more preferably greater thanabout 70% identity, still more preferably greater than about 80%, 85%,90%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity with a Magaprotein of the Ruminococcus torques, with the amino acid identitydetermined using a sequence alignment program such as CLUSTALW. Suchantigens can be prepared, for example, by purification from entericbacteria such as Ruminococcus torques, by recombinant expression of anucleic acid encoding a Maga peptide such as Uniprot No. D4M4S6, bysynthetic means such as solution or solid phase peptide synthesis, or byusing phage display.

The term “RtCpaF” refers to a Ruminococcus bromii Flp pilus assemblyprotein, ATPase CpF and that is immunoreactive with an anti-CpaFantibody. Suitable RbCpaF antigens useful in determining anti-CpaFantibody levels in a sample include, without limitation, a CpaF proteinof the Ruminococcus bromii, a CpaF polypeptide having substantially thesame amino acid sequence as the CpaF protein of the Ruminococcus bromii,or a fragment thereof such as an immunoreactive fragment thereof. A CpaFpolypeptide generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with a CpaF protein of theRuminococcus bromii, with the amino acid identity determined using asequence alignment program such as CLUSTALW. Such antigens can beprepared, for example, by purification from enteric bacteria such as R.bromii, by recombinant expression of a nucleic acid encoding a CpaFpeptide such as Uniprot No. D4L5L7, by synthetic means such as solutionor solid phase peptide synthesis, or by using phage display.

The term “RtPilD” refers to a Ruminococcus gnavus pilin isopeptidelinkage domain protein and that is immunoreactive with an anti-PilDantibody. Suitable RgPilD antigens useful in determining anti-PilDantibody levels in a sample include, without limitation, a PilD proteinof the Ruminococcus gnavus, a PilD polypeptide having substantially thesame amino acid sequence as the PilD protein of the Ruminococcus gnavus,or a fragment thereof such as an immunoreactive fragment thereof. A PilDpolypeptide generally describes polypeptides having an amino acidsequence with greater than about 50% identity, preferably greater thanabout 60% identity, more preferably greater than about 70% identity,still more preferably greater than about 80%, 85%, 90%, 95%, 96%, 97%,98%, or 99% amino acid sequence identity with a PilD protein of theRuminococcus gnavus, with the amino acid identity determined using asequence alignment program such as CLUSTALW. Such antigens can beprepared, for example, by purification from enteric bacteria such as R.gnavus, by recombinant expression of a nucleic acid encoding a PilDpeptide such as Uniprot No. A7B5T4, by synthetic means such as solutionor solid phase peptide synthesis, or by using phage display.

The term “LaFrc” refers to a protein of the Lactobacillus acidophilusthat is immunoreactive with an anti-Frc antibody. Frc is predicted to bea formyl CoA transferase. Suitable Frc antigens useful in determininganti-Frc antibody levels in a sample include, without limitation, a Frcprotein of the L. acidophilus, a Frc polypeptide having substantiallythe same amino acid sequence as the Frc protein of the L. acidophilus,or a fragment thereof such as an immunoreactive fragment thereof. A Frcpolypeptide of the L. acidophilus generally describes polypeptideshaving an amino acid sequence with greater than about 50% identity,preferably greater than about 60% identity, more preferably greater thanabout 70% identity, still more preferably greater than about 80%, 85%,90%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity with a Frcprotein of the L. acidophilus, with the amino acid identity determinedusing a sequence alignment program such as CLUSTALW. Such antigens canbe prepared, for example, by purification from enteric bacteria such asL. acidophilus, by recombinant expression of a nucleic acid encoding aFrc peptide such as NCBI Ref. Seq. No. YP_193317 or UniProt. No. Q5FLY8,by synthetic means such as solution or solid phase peptide synthesis.

The term “LaEno” refers to a protein of the Lactobacillus acidophilusthat is immunoreactive with an anti-Eno antibody. Eno is predicted to bea phosphopyruvate hydratase (enolase). Suitable LaEno antigens useful indetermining anti-Eno antibody levels in a sample include, withoutlimitation, an Eno protein of the L. acidophilus, an Eno polypeptidehaving substantially the same amino acid sequence as the Eno protein ofthe L. acidophilus, or a fragment thereof such as an immunoreactivefragment thereof. An Eno polypeptide of the L. acidophilus generallydescribes polypeptides having an amino acid sequence with greater thanabout 50% identity, preferably greater than about 60% identity, morepreferably greater than about 70% identity, still more preferablygreater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acidsequence identity with a Eno protein of the L. acidophilus, with theamino acid identity determined using a sequence alignment program suchas CLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such as L. acidophilus, by recombinant expressionof a nucleic acid encoding an Eno peptide such as NCBI Ref. Seq. No.YP_193779 or UniProt. No. Q5FKM6, by synthetic means such as solution orsolid phase peptide synthesis.

The term “LjEFTu” refers to a protein of the Lactobacillus johnsoniithat is immunoreactive with an anti-EFTu antibody. EFTu is predicted tobe an elongation factor Tu. Suitable EFTu antigens useful in determininganti-EFTu antibody levels in a sample include, without limitation, anEFTu protein of the L. johnsonii, an EFTu polypeptide havingsubstantially the same amino acid sequence as the EFTu protein of the L.acidophilus, or a fragment thereof such as an immunoreactive fragmentthereof. An EFTu polypeptide of the L. johnsonii generally describespolypeptides having an amino acid sequence with greater than about 50%identity, preferably greater than about 60% identity, more preferablygreater than about 70% identity, still more preferably greater thanabout 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acid sequenceidentity with an EFTu protein of the L. johnsonii, with the amino acididentity determined using a sequence alignment program such as CLUSTALW.Such antigens can be prepared, for example, by purification from entericbacteria such as L. johnsonii, by recombinant expression of a nucleicacid encoding an EFTu peptide such as NCBI Ref. Seq. No. NP_964865 orUniProt. No. Q74JU6, by synthetic means such as solution or solid phasepeptide synthesis.

The term “BfOmpA” refers to a protein of the Bacteroides fragilis thatis immunoreactive with an anti-OmpA antibody. OmpA is predicted to be amajor outer membrane protein A. Suitable OmpA antigens useful indetermining anti-OmpA antibody levels in a sample include, withoutlimitation, an OmpA protein of the B. fragilis, an OmpA polypeptidehaving substantially the same amino acid sequence as the OmpA protein ofthe B. fragilis, or a fragment thereof such as an immunoreactivefragment thereof. An OmpA polypeptide of the B. fragilis generallydescribes polypeptides having an amino acid sequence with greater thanabout 50% identity, preferably greater than about 60% identity, morepreferably greater than about 70% identity, still more preferablygreater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acidsequence identity with an OmpA protein of the B. fragilis, with theamino acid identity determined using a sequence alignment program suchas CLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such as B. fragilis, by recombinant expression ofa nucleic acid encoding an OmpA peptide such as NCBI Ref. Seq. No.YP_098863 or UniProt. No. Q64VP7, by synthetic means such as solution orsolid phase peptide synthesis.

The term “PrOmpA” refers to a protein of the Prevotella species, e.g.,Prevotella sp. oral taxon 472 str. F0295, that is immunoreactive with ananti-OmpA antibody. OmpA is predicted to be a immunoreactive antigenPG33 or major outer membrane protein A. Suitable OmpA antigens useful indetermining anti-OmpA antibody levels in a sample include, withoutlimitation, an OmpA protein of the Prevotella sp., an OmpA polypeptidehaving substantially the same amino acid sequence as the OmpA protein ofthe Prevotella sp., or a fragment thereof such as an immunoreactivefragment thereof. An OmpA polypeptide of the Prevotella sp. generallydescribes polypeptides having an amino acid sequence with greater thanabout 50% identity, preferably greater than about 60% identity, morepreferably greater than about 70% identity, still more preferablygreater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acidsequence identity with an OmpA protein of the Prevotella sp., with theamino acid identity determined using a sequence alignment program suchas CLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such as Prevotella sp., by recombinant expressionof a nucleic acid encoding an OmpA peptide such as NCBI GenBankAccession No. EEX54413 or UniProt. No. C9PT48, by synthetic means suchas solution or solid phase peptide synthesis.

The term “Cp10bA” refers to a protein of the Clostridia perfringens thatis immunoreactive with an anti-10bA antibody. 10bA is predicted to be a10b antigen. Suitable 10bA antigens useful in determining anti-10bAantibody levels in a sample include, without limitation, a 10bA proteinof the C. perfringens, a 10bA polypeptide having substantially the sameamino acid sequence as the 10bA protein of the C. perfringens, or afragment thereof such as an immunoreactive fragment thereof. A 10bApolypeptide of the C. perfringens generally describes polypeptideshaving an amino acid sequence with greater than about 50% identity,preferably greater than about 60% identity, more preferably greater thanabout 70% identity, still more preferably greater than about 80%, 85%,90%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity with a 10bAprotein of the C. perfringens, with the amino acid identity determinedusing a sequence alignment program such as CLUSTALW. Such antigens canbe prepared, for example, by purification from enteric bacteria such asC. perfringens, by recombinant expression of a nucleic acid encoding a10bA peptide such as NCBI GenBank Accession No. EDT72304 or UniProt. No.B1V1I2, by synthetic means such as solution or solid phase peptidesynthesis.

The term “CpSpA” refers to a protein of the Clostridia perfringens thatis immunoreactive with an anti-SpA antibody. SpA is predicted to be asurface protective antigen SpA homolog. Suitable SpA antigens useful indetermining anti-SpA antibody levels in a sample include, withoutlimitation, a SpA protein of the C. perfringens, a SpA polypeptidehaving substantially the same amino acid sequence as the SpA protein ofthe C. perfringens, or a fragment thereof such as an immunoreactivefragment thereof. A SpA polypeptide of the C. perfringens generallydescribes polypeptides having an amino acid sequence with greater thanabout 50% identity, preferably greater than about 60% identity, morepreferably greater than about 70% identity, still more preferablygreater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acidsequence identity with a SpA protein of the C. perfringens, with theamino acid identity determined using a sequence alignment program suchas CLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such as C. perfringens, by recombinant expressionof a nucleic acid encoding a SpA peptide such as NCBI Ref. Seq. No.YP_209686 or UniProt. No. Q5DWA9, by synthetic means such as solution orsolid phase peptide synthesis.

The term “EfSant” refers to a protein of the Enterococcus faecalis thatis immunoreactive with an anti-Sant antibody. Sant is predicted to be asurface antigen. Suitable Sant antigens useful in determining anti-Santantibody levels in a sample include, without limitation, a Sant proteinof the E. faecalis, a Sant polypeptide having substantially the sameamino acid sequence as the Sant protein of the E. faecalis, or afragment thereof such as an immunoreactive fragment thereof. A Santpolypeptide of the E. faecalis generally describes polypeptides havingan amino acid sequence with greater than about 50% identity, preferablygreater than about 60% identity, more preferably greater than about 70%identity, still more preferably greater than about 80%, 85%, 90%, 95%,96%, 97%, 98%, or 99% amino acid sequence identity with a Sant proteinof the E. faecalis, with the amino acid identity determined using asequence alignment program such as CLUSTALW. Such antigens can beprepared, for example, by purification from enteric bacteria such as E.faecalis, by recombinant expression of a nucleic acid encoding a Santpeptide such as NCBI GenBank Accession No. EEU72780 or UniProt. No.C7W575, by synthetic means such as solution or solid phase peptidesynthesis.

The term “LmOsp” refers to a protein of the Listeria monocytogenes thatis immunoreactive with an anti-Osp antibody. Osp is predicted to be anouter surface antigen. Suitable Osp antigens useful in determininganti-Osp antibody levels in a sample include, without limitation, an Ospprotein of the L. monocytogenes, an Osp polypeptide having substantiallythe same amino acid sequence as the Osp protein of the L. monocytogenes,or a fragment thereof such as an immunoreactive fragment thereof. An Osppolypeptide of the L. monocytogenes generally describes polypeptideshaving an amino acid sequence with greater than about 50% identity,preferably greater than about 60% identity, more preferably greater thanabout 70% identity, still more preferably greater than about 80%, 85%,90%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity with an Ospprotein of the L. monocytogenes, with the amino acid identity determinedusing a sequence alignment program such as CLUSTALW. Such antigens canbe prepared, for example, by purification from enteric bacteria such asL. monocytogenes, by recombinant expression of a nucleic acid encodingan Osp peptide such as NCBI Ref. Seq. No. YP_002349810 or UniProt. No.B8DFK3, by synthetic means such as solution or solid phase peptidesynthesis.

The term “SfET-2” refers to a protein of the S. flexneri that isimmunoreactive with an anti-enterotoxin ET-2 antibody. ET-2 is predictedto be an enterotoxin. Suitable ET-2 antigens useful in determininganti-ET-2 antibody levels in a sample include, without limitation, anET-2 protein of the S. flexneri, an ET-2 polypeptide havingsubstantially the same amino acid sequence as the ET-2 protein of the S.flexneri, or a fragment thereof such as an immunoreactive fragmentthereof. An ET-2 polypeptide of the S. flexneri generally describespolypeptides having an amino acid sequence with greater than about 50%identity, preferably greater than about 60% identity, more preferablygreater than about 70% identity, still more preferably greater thanabout 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acid sequenceidentity with an ET-2 protein of the S. flexneri, with the amino acididentity determined using a sequence alignment program such as CLUSTALW.Such antigens can be prepared, for example, by purification from entericbacteria such S. flexneri, by recombinant expression of a nucleic acidencoding an ET-2 peptide such as UniProt. No. Q7BEN0, by synthetic meanssuch as solution or solid phase peptide synthesis.

The term “Cpatox” refers to a protein of the C. perfringens that isimmunoreactive with an anti-alpha toxin antibody. αtox is predicted tobe an alpha toxin. Suitable αtox antigens useful in determininganti-αtox antibody levels in a sample include, without limitation, anαtox protein of the C. perfringens, an αtox polypeptide havingsubstantially the same amino acid sequence as the αtox protein of the C.perfringens, or a fragment thereof such as an immunoreactive fragmentthereof. An αtox polypeptide of the C. perfringens generally describespolypeptides having an amino acid sequence with greater than about 50%identity, preferably greater than about 60% identity, more preferablygreater than about 70% identity, still more preferably greater thanabout 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acid sequenceidentity with an αtox protein of the C. perfringens, with the amino acididentity determined using a sequence alignment program such as CLUSTALW.Such antigens can be prepared, for example, by purification from entericbacteria such C. perfringens, by recombinant expression of a nucleicacid encoding an αtox peptide such as UniProt. No. Q3HR45, by syntheticmeans such as solution or solid phase peptide synthesis.

The term “Cpbtox” refers to a protein of the C. perfringens that isimmunoreactive with an anti-beta2 toxin antibody. βtox is predicted tobe a beta toxin. Suitable βtox antigens useful in determining anti-βtoxantibody levels in a sample include, without limitation, a βtox proteinof the C. perfringens, an βtox polypeptide having substantially the sameamino acid sequence as the βtox protein of the C. perfringens, or afragment thereof such as an immunoreactive fragment thereof. A βtoxpolypeptide of the C. perfringens generally describes polypeptideshaving an amino acid sequence with greater than about 50% identity,preferably greater than about 60% identity, more preferably greater thanabout 70% identity, still more preferably greater than about 80%, 85%,90%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity with anβtox protein of the C. perfringens, with the amino acid identitydetermined using a sequence alignment program such as CLUSTALW. Suchantigens can be prepared, for example, by purification from entericbacteria such C. perfringens, by recombinant expression of a nucleicacid encoding an βtox peptide such as UniProt. No. B1R976, by syntheticmeans such as solution or solid phase peptide synthesis.

The term “EcSta2” refers to a protein of the E. coli that isimmunoreactive with an anti-heat stable toxin Sta2 antibody. Sta2 ispredicted to be a heat-stable toxin. Suitable Sta2 antigens useful indetermining anti-Sta2 antibody levels in a sample include, withoutlimitation, a Sta2 protein of the E. coli, a Sta2 polypeptide havingsubstantially the same amino acid sequence as the Sta2 protein of the E.coli, or a fragment thereof such as an immunoreactive fragment thereof.A Sta2 polypeptide of the E. coli generally describes polypeptideshaving an amino acid sequence with greater than about 50% identity,preferably greater than about 60% identity, more preferably greater thanabout 70% identity, still more preferably greater than about 80%, 85%,90%, 95%, 96%, 97%, 98%, or 99% amino acid sequence identity with a Sta2protein of the E. coli, with the amino acid identity determined using asequence alignment program such as CLUSTALW. Such antigens can beprepared, for example, by purification from enteric bacteria such E.coli, by recombinant expression of a nucleic acid encoding a Sta2peptide such as UniProt. No. Q2WE95, by synthetic means such as solutionor solid phase peptide synthesis.

The term “EcOStx2a” refers to a protein of the E. coli H7:O157 that isimmunoreactive with an anti-Stx2a antibody. Stx2a is predicted to be aShiga toxin subunit A. Suitable Stx2a antigens useful in determininganti-Stx2a antibody levels in a sample include, without limitation, aStx2a protein of the E. coli H7:O157, a Stx2a polypeptide havingsubstantially the same amino acid sequence as the Stx2a protein of theE. coli, or a fragment thereof such as an immunoreactive fragmentthereof. A Stx2a polypeptide of the E. coli H7:O157 generally describespolypeptides having an amino acid sequence with greater than about 50%identity, preferably greater than about 60% identity, more preferablygreater than about 70% identity, still more preferably greater thanabout 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99% amino acid sequenceidentity with a Stx2a protein of the E. coli H7:O157, with the aminoacid identity determined using a sequence alignment program such asCLUSTALW. Such antigens can be prepared, for example, by purificationfrom enteric bacteria such E. coli H7:O157, by recombinant expression ofa nucleic acid encoding a Stx2a peptide such as UniProt. No. B6ZXF5, bysynthetic means such as solution or solid phase peptide synthesis.

The term “CjCdtB/C” refers to a protein of the Campylobacter jejuni thatis immunoreactive with an anti-CdtB/C antibody. CdtB is predicted to bea cytolethal distending toxin subunit B and CdtC is predicted to be acytolethal distending toxin subunit C. Suitable CdtB/C antigens usefulin determining anti-CdtB/C antibody levels in a sample include, withoutlimitation, a CdtB/C protein of the C. jejuni, a CdtB/C polypeptidehaving substantially the same amino acid sequence as the CdtB/C aprotein of the C. jejuni, or a fragment thereof such as animmunoreactive fragment thereof. A CdtB/C polypeptide of the C. jejunigenerally describes polypeptides having an amino acid sequence withgreater than about 50% identity, preferably greater than about 60%identity, more preferably greater than about 70% identity, still morepreferably greater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99%amino acid sequence identity with a CdtB/C protein of the C. jejuni,with the amino acid identity determined using a sequence alignmentprogram such as CLUSTALW. Such antigens can be prepared, for example, bypurification from enteric bacteria such C. jejuni, by recombinantexpression of a nucleic acid encoding a CdtB/C peptide such as UniProt.Nos. Q46101 and Q46102, by synthetic means such as solution or solidphase peptide synthesis.

The term “CjCdA/B” refers to a protein of the Clostridium difficile thatis immunoreactive with an anti-toxinA/B antibody. CdA is predicted to betoxinA and CdB is predicted to be toxin B. Suitable CdA/B antigensuseful in determining anti-CdA/B antibody levels in a sample include,without limitation, a CdA/B protein of the C. difficile, a CdA/Bpolypeptide having substantially the same amino acid sequence as theCdA/B a protein of the C. difficile, or a fragment thereof such as animmunoreactive fragment thereof. A CdA/B polypeptide of the C. difficilegenerally describes polypeptides having an amino acid sequence withgreater than about 50% identity, preferably greater than about 60%identity, more preferably greater than about 70% identity, still morepreferably greater than about 80%, 85%, 90%, 95%, 96%, 97%, 98%, or 99%amino acid sequence identity with a CdA/B protein of the C. difficile,with the amino acid identity determined using a sequence alignmentprogram such as CLUSTALW. Such antigens can be prepared, for example, bypurification from enteric bacteria such C. difficile, by recombinantexpression of a nucleic acid encoding a CdA/B peptide such as UniProt.Nos. P16154 and P18177, by synthetic means such as solution or solidphase peptide synthesis.

In some embodiments, the method comprises determining the level of atleast one bacterial antigen antibody marker, e.g., 1, 2, 3, 4, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,26, 27, 28, 29, 30, 31, 32, 33, 34, or 35 markers by measuring the levelof antibody against the bacterial antigen present in a sample from anindividual. In some instances, if an individual possessing at least onebacterial antigen antibody at a level that is higher or lower than ahealthy control, it is indicative of the individual having IBS. Thepresence or level of the bacterial antigen antibody in the individualcan be correlated to the level of the disease.

In some embodiments, the level of at least one antibody against acommensal bacterial antigen in a patient sample indicates that thepatient has IBS, wherein the commensal bacterial antigen is selectedfrom the group consisting of LaFrc, LaEno, LjEFTu, BfOmpA, PrOmpA,Cp10bA, CpSpA, EfSant, LmOsp, and combinations thereof. If it isdetermined that the level of an antibody against a bacterial antigenfrom the bacterial class Bacteroides fragilis or Prevotella sp. isreduced in a sample taken from an individual, compared to the level froma normal control sample, then the individual is diagnosed as having IBS.If it is determined that the level of an antibody against a bacterialantigen from the bacterial class selected from the group consisting ofClostridia perfringens, Enterococcus, Listeria and other Firmicutesclasses is higher in an individual's sample compared to a healthycontrol sample, then the individual is diagnosed as having IBS.

In some embodiments, subjects with a lower level of antibodies againstBacteriodetes class bacteria compared to healthy controls are morelikely to have IBS. In contrast, subjects with a higher level ofantibodies against Clostridia, Mollicutes and/or Bacilli class bacteriacompared to healthy control may be more likely to have IBS.

In other embodiments, the method provided herein is used to determinethat a subject with an increased level (e.g., amount, concentration,ratio) of antibodies to a Firmicutes antigen such as a LaFrc, LaEno,LjEfTu, Cp10ba, CpSpaA, EfSant, and LmOsp antigen, over an Bacteriodetesantigen, such as a BJOmpA and PrOmpA antigen, is predicted that thesubject has an increased likelihood of having IBS.

C. Mast Cell Markers

1. β-Tryptase

In one aspect, a method for aiding in the diagnosis of irritable bowelsyndrome (IBS) in a subject is provided, the method comprises: a)determining the level of β-tryptase present in a sample from a subject;and (b) comparing the level of β-tryptase present in the sample to thatof a control level, wherein an increased level of β-tryptase present inthe sample from the subject is indicative of an increase likelihood ofthe subjecting having IBS.

In some embodiments, the method of determining the level of β-tryptasepresent in a sample from a subject comprises: (a) contacting abiological sample from the subject with a β-tryptase binding moietyunder conditions suitable to transform β-tryptase present in the sampleinto a complex comprising β-tryptase and the β-tryptase binding moiety;and (b) determining the level of the complex, thereby determining thelevel of β-tryptase present in the sample.

In a specific embodiments, the method of determining the level ofβ-tryptase present in a sample from a subject comprises: (a) contactinga sample having β-tryptase contained therein under conditions suitableto transform the β-tryptase into a complex comprising β-tryptase and acapture anti-tryptase antibody; (b) contacting the complex with anenzyme labeled indicator antibody to transform the complex into alabeled complex; (c) contacting the labeled complex with a substrate forthe enzyme; and (d) detecting the presence or level of β-tryptase in thesample.

An exemplary embodiment of a method for determining the level ofβ-tryptase present in a sample from a subject is described in U.S. Pat.No. 8,114,616, the disclosure of which is herein incorporated byreference in its entirety for all purposes.

In preferred embodiments, β-tryptase, histamine, and/or PGE2 aredetected from the same sample, although in certain instances thebiomarkers may be detected in samples taken from the same individual,for example, at the same time or at different times. In certainembodiments, the biomarkers are detected in separate assays performedwith different aliquots of a blood or serum sample from a subject. Inother embodiments, the biomarkers are detected in a single multiplexdetection assay, for example, in a Luminex xAMP assay.

2. Histamine

In a specific embodiment, the present invention provides a method to aidin the diagnosis of IBS, the method comprises: (a) contacting a samplehaving histamine contained therein under conditions suitable totransform the acetylated histamine into a complex comprising histamineand a capture anti-histamine antibody; (b) contacting the complex withan enzyme labeled indicator antibody to transform the complex into alabeled complex; (c) contacting the labeled complex with a substrate forthe enzyme; and (d) detecting the level of histamine in the sample.

An exemplary embodiment of the assay is a histamine enzyme immunoassaysuch as the EIA Histamine Assay (Cat. No. IM2015, ImmunoTech). Briefly,histamine present in the sample, calibrator or control is acetylated byadmixing 25 μl of acylation buffer, 100 μl of samples, calibrators orcontrols, and 25 μl of acylation reagent, and vortexing immediately. 50μl of the acylated samples, calibrators or controls are added to theanti-histamine antibody coated wells of the microtiter assay plate.Then, 200 μl of alkaline phosphatase-histamine conjugate is added to theplate. The plate is incubated for 2 hours at 2-8° C. with shaking. Thewells are washed with wash solution, and 200 μl of chromogenic substrateis added to the wells. The plate is incubated for 30 minutes at 18-25°C. in the dark with shaking. Then, 50 μl of reaction stop solution isadded before reading the luminescence with a luminescence plate reader.The Relative Luminescent Unit (RLU) and the histamine concentration ofthe calibrators are plotted using graphing software such as Graphpad(Prism). The levels of histamine in the sample and control arecalculated by interpolation from a calibrator curve that is performed inthe same assay as the sample.

3. Prostaglandin E2

In a specific embodiment, the present invention provides a method to aidin the diagnosis of IBS, the assay comprising: (a) contacting a samplehaving prostaglandin E2 contained therein under conditions suitable totransform the prostaglandin E2 into a complex comprising prostaglandinE2 and a capture anti-prostaglandin E2 antibody; (b) contacting thecomplex with an enzyme labeled indicator antibody to transform thecomplex into a labeled complex; (c) contacting the labeled complex witha substrate for the enzyme; and (d) detecting the level of prostaglandinE2 in the sample.

An exemplary embodiment of the assay is a PGE2 competitive enzymeimmunoassay such as the Prostaglandin E2 EIA Kit-Monoclonal (Cat. No.514010, Cayman Chemical). Briefly, 50 μl of calibrator (standard) orsample is added to wells of a precoated goat anti-mouse IgG microtiterassay plate. 50 μl of PGE2 tracer (covalently conjugated PGE2 andacetylcholinesterase) is added, and then 50 μl of anti-PGE2 mouse IgG.The plate is incubated for 18 hours at 4° C. with shaking. The plate iswashed 5 times with wash buffer. 200 μl of developing reagent (e.g.,Ellman's reagent) is added to the wells. The plate is incubated for60-90 minutes in the dark with shaking. Luminescence is read at 405 nmwith a luminescence plate reader. The Relative Luminescent Unit (RLU)and the prostaglandin E2 concentration of the calibrators are plottedusing graphing software such as GraphPad Prism (GraphPad Software, LaJolla, Calif.). The levels of prostaglandin E2 in the sample and controlare calculated by interpolation from a calibrator curve that isperformed in the same assay as the sample.

D. Bile Acid Malabsorption Markers

In some embodiments, bile acid malabsorption (BAM) markers for use inthe present invention are selected from a group consisting of bile acid,FXR, cholesterol, 7α-hydroxy-4-cholesten-3-one (C4), FGF19, CYP7A, and acombination thereof.

In some embodiments, level of a BAM marker such as7α-hydroxy-4-cholesten-3-one and FGF19 is detected by a competitiveenzyme immunoassay. In some instances, an antibody against7α-hydroxy-4-cholesten-3-one is used. In some instances, an antibodyagainst FGF19 is used. Assays for measuring 7α-hydroxy-4-cholesten-3-oneare described in, e.g., PCT Application No. PCT/IB2014/061634, filed May27, 2014. Other methods for measuring 7α-hydroxy-4-cholesten-3-oneinclude high pressure liquid chromatography, tandem mass spectrometry(HPLC-MS/MS) described in, e.g., Camilleri et al., Neurogastroenterol i,2009, 21(7):734-e43 or electrospray ionization liquidchromatography-tandem mass spectrometry (ESI-LC-MS/MS) described in,e.g., Honda et al., J Lipid Research, 2007, 48:458-464.

E. Serotonin Markers

In some embodiments, the serotonin markers for use in the presentinvention are selected from a group consisting of serotonin (5-HT),5-hydroxyindoleacetic acid (5-HIAA), serotonin-O-sulfate,serotonin-O-phosphate, and a combination thereof. The level of one ormore of the serotonin markers can be measured using a competitive enzymeimmunoassay. In some instances, a derivative or analog of the serotoninmarker is used in the assay. In other instances, an antibody againstserotonin or a metabolite thereof can be used to detect the serotoninmarker in a biological sample from an individual. Assays for measuringserotonin and metabolites thereof are described in, e.g., PCTApplication No. PCT/IB2014/061634, filed May 22, 2014.

Levels of serotonin and metabolites thereof can be measured by othermethods such liquid chromatography, e.g., HPLC/MS, or immunologicalmethods such as using commercially available serotonin-specificantibodies from, for example, Abcam (Cambridge, Mass.), Dako(Carpinteria, Calif.), and Santa Cruz Biotechnology (Santa Cruz,Calif.).

In some embodiments, the sample is derivatized to increase the stabilityof serotonin and metabolites thereof prior to measuring their levels.For instance, the sample can be mixed with a derivatization mix, such asone containing 0.1M CAPS buffer (pH 11.0), 0.1M p-(aminomethyl)benzylcompound, 0.05 m potassium hexacyanoferrate (III), and methanol at aratio of 10:11:22:23 (v:v:v:v).

F. Kynurenine Markers

Irregularities of serotonin function in IBS may be due to changes in themetabolism of the serotonin precursor L-tryptophan. Tryptophan is anessential amino acid that serves as a precursor to serotonin but whichcan alternatively be metabolized along the kynurenine pathway. This, inturn, leads to the production of other neuroactive agents. It has beenshown that kynurenine levels and the kynurenine:tryptophan ratio areincreased in IBS.

In some embodiments, the kynurenine markers for use in the presentinvention are selected from a group consisting of kynurenine (K),kynurenic acid (KyA), anthranilic acid (AA), 3-hydroxykynurenine (3-HK),3-hydroxyanthranilic acid (3-HAA), xanthurenic acid (XA), quinolinicacid (QA), tryptophan, 5-hydroxytryptophan (5-HTP), and a combinationthereof. The level of one or more of the kynurenine markers can bemeasured using a competitive enzyme immunoassay. In some instances, aderivative or analog of the kynurenine marker is used in the assay. Inother instances, an antibody against kynurenine or a metabolite thereofcan be used to detect the kynurenine marker in a biological sample froman individual. Assays for measuring tryptophan, kynurenine andmetabolites thereof are described in, e.g., PCT Application No.PCT/IB32014/061634. Levels of the kynurenine markers can also bemeasured by other methods such liquid chromatography, e.g., HPLC,HPLC/MS, and the like.

G. Inflammatory Markers

A variety of inflammatory markers, including biochemical markers,serological markers, protein markers, and other clinicalcharacteristics, are suitable for use in the methods of the presentinvention for diagnosing IBS and/or subtypes thereof. In certainaspects, the methods described herein utilize the application of analgorithm (e.g., statistical analysis) to the presence, concentrationlevel, and/or genotype determined for one or more of the inflammatorymarkers to aid or assist in predicting that a subject has IBS and/or asubtype thereof.

Non-limiting examples of inflammatory markers include: biochemical,serological, and protein markers such as, e.g., cytokines includinginterleukins, acute phase proteins, cellular adhesion molecules, andcombinations thereof.

1. Cytokines

The determination of the presence or level of at least one cytokine in asample is particularly useful in the present invention. As used herein,the term “cytokine” includes any of a variety of polypeptides orproteins secreted by immune cells that regulate a range of immune systemfunctions and encompasses small cytokines such as chemokines. The term“cytokine” also includes adipocytokines, which comprise a group ofcytokines secreted by adipocytes that function, for example, in theregulation of body weight, hematopoiesis, angiogenesis, wound healing,insulin resistance, the immune response, and the inflammatory response.

In certain aspects, the presence or level of at least one cytokineincluding, but not limited to, TNF-α, TNF-related weak inducer ofapoptosis (TWEAK), osteoprotegerin (OPG), IFN-α, IFN-β, IFN-γ, IL-1α,IL-1β, IL-1 receptor antagonist (IL-1ra), IL-2, IL-4, IL-5, IL-6,soluble IL-6 receptor (sIL-6R), IL-7, IL-8, IL-9, IL-10, IL-12, IL-13,IL-15, IL-17, IL-23, and IL-27 is determined in a sample. In certainother aspects, the presence or level of at least one chemokine such as,for example, CXCL1/GRO1/GROα, CXCL2/GRO2, CXCL3/GRO3, CXCL4/PF-4,CXCL5/ENA-78, CXCL6/GCP-2, CXCL7/NAP-2, CXCL9/MIG, CXCL10/IP-10,CXCL11/I-TAC, CXCL12/SDF-1, CXCL13/BCA-1, CXCL14/BRAK, CXCL15, CXCL16,CXCL17/DMC, CCL1, CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, CCL5/RANTES,CCL6/C10, CCL7/MCP-3, CCL8/MCP-2, CCL9/CCL10, CCL11/Eotaxin,CCL12/MCP-5, CCL13/MCP-4, CCL14/HCC-1, CCL15/MIP-5, CCL16/LEC,CCL17/TARC, CCL18/MIP-4, CCL19/MIP-3β, CCL20/MIP-3α, CCL21/SLC,CCL22/MDC, CCL23/MPIF1, CCL24/Eotaxin-2, CCL25/TECK, CCL26/Eotaxin-3,CCL27/CTACK, CCL28/MEC, CL1, CL2, and CX₃CL1 is determined in a sample.In certain further aspects, the presence or level of at least oneadipocytokine including, but not limited to, leptin, adiponectin,resistin, active or total plasminogen activator inhibitor-1 (PAI-1),visfatin, and retinol binding protein 4 (RBP4) is determined in asample. Preferably, the presence or level of TNFα, IL-6, IL-1β, IFN-γ,and/or IL-10 is determined.

In certain instances, the presence or level of a particular cytokine isdetected at the level of mRNA expression with an assay such as, forexample, a hybridization assay or an amplification-based assay. Incertain other instances, the presence or level of a particular cytokineis detected at the level of protein expression using, for example, animmunoassay (e.g., ELISA) or an immunohistochemical assay. SuitableELISA kits for determining the presence or level of a cytokine such asIL-6, IL-1β, or TWEAK in a serum, plasma, saliva, or urine sample areavailable from, e.g., R&D Systems, Inc. (Minneapolis, Minn.), NeogenCorp. (Lexington, Ky.), Alpco Diagnostics (Salem, N.H.), Assay Designs,Inc. (Ann Arbor, Mich.), BD Biosciences Pharmingen (San Diego, Calif.),Invitrogen (Camarillo, Calif.), Calbiochem (San Diego, Calif.), CHEMICONInternational, Inc. (Temecula, Calif.), Antigenix America Inc.(Huntington Station, N.Y.), QIAGEN Inc. (Valencia, Calif.), Bio-RadLaboratories, Inc. (Hercules, Calif.), and/or Bender MedSystems Inc.(Burlingame, Calif.).

2. Acute Phase Proteins

The determination of the presence or level of one or more acute-phaseproteins in a sample is also useful in the present invention.Acute-phase proteins are a class of proteins whose plasma concentrationsincrease (positive acute-phase proteins) or decrease (negativeacute-phase proteins) in response to inflammation. This response iscalled the acute-phase reaction (also called acute-phase response).Examples of positive acute-phase proteins include, but are not limitedto, C-reactive protein (CRP), D-dimer protein, mannose-binding protein,alpha 1-antitrypsin, alpha 1-antichymotrypsin, alpha 2-macroglobulin,fibrinogen, prothrombin, factor VIII, von Willebrand factor,plasminogen, complement factors, ferritin, serum amyloid P component,serum amyloid A (SAA), orosomucoid (alpha 1-acid glycoprotein, AGP),ceruloplasmin, haptoglobin, and combinations thereof. Non-limitingexamples of negative acute-phase proteins include albumin, transferrin,transthyretin, transcortin, retinol-binding protein, and combinationsthereof. Preferably, the presence or level of CRP and/or SAA isdetermined.

In certain instances, the presence or level of a particular acute-phaseprotein is detected at the level of mRNA expression with an assay suchas, for example, a hybridization assay or an amplification-based assay.In certain other instances, the presence or level of a particularacute-phase protein is detected at the level of protein expressionusing, for example, an immunoassay (e.g., ELISA) or animmunohistochemical assay. For example, a sandwich colorimetric ELISAassay available from Alpco Diagnostics (Salem, N.H.) can be used todetermine the level of CRP in a serum, plasma, urine, or stool sample.Similarly, an ELISA kit available from Biomeda Corporation (Foster City,Calif.) can be used to detect CRP levels in a sample. Other methods fordetermining CRP levels in a sample are described in, e.g., U.S. Pat.Nos. 6,838,250 and 6,406,862; and U.S. Patent Publication Nos.20060024682 and 20060019410. Additional methods for determining CRPlevels include, e.g., immunoturbidimetry assays, rapid immunodiffusionassays, and visual agglutination assays. Suitable ELISA kits fordetermining the presence or level of SAA in a sample such as serum,plasma, saliva, urine, or stool are available from, e.g., AntigenixAmerica Inc. (Huntington Station, N.Y.), Abazyme (Needham, Mass.), USCNLife (Missouri City, Tex.), and/or U.S. Biological (Swampscott, Mass.).

C-reactive protein (CRP) is a protein found in the blood in response toinflammation (an acute-phase protein). CRP is typically produced by theliver and by fat cells (adipocytes). It is a member of the pentraxinfamily of proteins. The human CRP polypeptide sequence is set forth in,e.g., Genbank Accession No. NP_000558. The human CRP mRNA (coding)sequence is set forth in, e.g., Genbank Accession No. NM_000567. Oneskilled in the art will appreciate that CRP is also known as PTX1,MGC88244, and MGC149895.

Serum amyloid A (SAA) proteins are a family of apolipoproteinsassociated with high-density lipoprotein (HDL) in plasma. Differentisoforms of SAA are expressed constitutively (constitutive SAAs) atdifferent levels or in response to inflammatory stimuli (acute phaseSAAs). These proteins are predominantly produced by the liver. Theconservation of these proteins throughout invertebrates and vertebratessuggests SAAs play a highly essential role in all animals. Acute phaseserum amyloid A proteins (A-SAAs) are secreted during the acute phase ofinflammation. The human SAA polypeptide sequence is set forth in, e.g.,Genbank Accession No. NP_000322. The human SAA mRNA (coding) sequence isset forth in, e.g., Genbank Accession No. NM_000331. One skilled in theart will appreciate that SAA is also known as PIG4, TP53I4, MGC111216,and SAA1.

3. Cellular Adhesion Molecules (IgSF CAMs)

The determination of the presence or level of one or more immunoglobulinsuperfamily cellular adhesion molecules in a sample is also useful inthe present invention. As used herein, the term “immunoglobulinsuperfamily cellular adhesion molecule” (IgSF CAM) includes any of avariety of polypeptides or proteins located on the surface of a cellthat have one or more immunoglobulin-like fold domains, and whichfunction in intercellular adhesion and/or signal transduction. In manycases, IgSF CAMs are transmembrane proteins. Non-limiting examples ofIgSF CAMs include Neural Cell Adhesion Molecules (NCAMs; e.g., NCAM-120,NCAM-125, NCAM-140, NCAM-145, NCAM-180, NCAM-185, etc.), IntercellularAdhesion Molecules (ICAMs, e.g., ICAM-1, ICAM-2, ICAM-3, ICAM-4, andICAM-5), Vascular Cell Adhesion Molecule-1 (VCAM-1),Platelet-Endothelial Cell Adhesion Molecule-1 (PECAM-1), L1 CellAdhesion Molecule (L1CAM), cell adhesion molecule with homology to L1CAM(close homolog of L1) (CHL1), sialic acid binding Ig-like lectins(SIGLECs; e.g., SIGLEC-1, SIGLEC-2, SIGLEC-3, SIGLEC-4, etc.), Nectins(e.g., Nectin-1, Nectin-2, Nectin-3, etc.), and Nectin-like molecules(e.g., Ned-1, Necl-2, Necl-3, Necl-4, and Necl-5). Preferably, thepresence or level of ICAM-1 and/or VCAM-1 is determined.

ICAM-1 is a transmembrane cellular adhesion protein that is continuouslypresent in low concentrations in the membranes of leukocytes andendothelial cells. Upon cytokine stimulation, the concentrations greatlyincrease. ICAM-1 can be induced by IL-1 and TNFα and is expressed by thevascular endothelium, macrophages, and lymphocytes. In IBD,proinflammatory cytokines cause inflammation by upregulating expressionof adhesion molecules such as ICAM-1 and VCAM-1. The increasedexpression of adhesion molecules recruit more lymphocytes to theinfected tissue, resulting in tissue inflammation (see, Goke et al., J.,Gastroenterol., 32:480 (1997); and Rijcken et al., Gut, 51:529 (2002)).ICAM-1 is encoded by the intercellular adhesion molecule 1 gene (ICAM1;Entrez GeneID:3383; Genbank Accession No. NM_000201) and is producedafter processing of the intercellular adhesion molecule 1 precursorpolypeptide (Genbank Accession No. NP_000192).

VCAM-1 is a transmembrane cellular adhesion protein that mediates theadhesion of lymphocytes, monocytes, eosinophils, and basophils tovascular endothelium. Upregulation of VCAM-1 in endothelial cells bycytokines occurs as a result of increased gene transcription (e.g., inresponse to Tumor necrosis factor-alpha (TNFα) and Interleukin-1(IL-1)). VCAM-1 is encoded by the vascular cell adhesion molecule 1 gene(VCAM1; Entrez GeneID:7412) and is produced after differential splicingof the transcript (Genbank Accession No. NM_001078 (variant 1) orNM_080682 (variant 2)), and processing of the precursor polypeptidesplice isoform (Genbank Accession No. NP_001069 (isoform a) or NP_542413(isoform b)).

In certain instances, the presence or level of an IgSF CAM is detectedat the level of mRNA expression with an assay such as, for example, ahybridization assay or an amplification-based assay. In certain otherinstances, the presence or level of an IgSF CAM is detected at the levelof protein expression using, for example, an immunoassay (e.g., ELISA)or an immunohistochemical assay. Suitable antibodies and/or ELISA kitsfor determining the presence or level of ICAM-1 and/or VCAM-1 in asample such as a tissue sample, biopsy, serum, plasma, saliva, urine, orstool are available from, e.g., Invitrogen (Camarillo, Calif.), SantaCruz Biotechnology, Inc. (Santa Cruz, Calif.), and/or Abcam Inc.(Cambridge, Mass.).

H. Diagnostic Model

In some embodiments of the present invention, a diagnostic model isestablished using a retrospective cohort with known outcomes of aclinical subtype of IBS and healthy controls. In some instances, thediagnostic model comprises an oxidative stress score, a mast cell score,a serotonin score, a BAM score, a microbiome score, and a inflammatoryscore. The diagnostic model is generated by applying the retrospectivedata on individuals with IBS and healthy controls to statisticalalgorithms. In some embodiments, the oxidative stress score is derivedby applying logistic regression analysis to the level of one or morekynurenine markers determined in a retrospective cohort. In someembodiments, the mast cell score is derived by applying logisticregression analysis to the level of one or more mast cell markersdetermined in a retrospective cohort. In some embodiments, the serotoninscore is derived by applying logistic regression analysis to the levelof one or more serotonin markers determined in a retrospective cohort.In some embodiments, the bile acid malabsorption score is derived byapplying logistic regression analysis to the level of one or more bileacid malabsorption markers determined in a retrospective cohort. In someembodiments, the microbiome score is derived by applying logisticregression analysis to the level of one or more bacterial antigenantibody markers determined in a retrospective cohort. In someembodiments, the inflammatory score is derived by applying logisticregression analysis to the level of one or more inflammatory markersdetermined in a retrospective cohort. For instance, a diagnostic modelwas generated using retrospective data of kynurenine markers, mast cellmarkers, serotonin markers, BAM markers, bacterial antigen antibodymarkers and inflammatory markers, in combination with a logisticregression machine learning algorithm.

I. Statistical Analysis

In certain instances, the statistical algorithm or statistical analysisis a learning statistical classifier system. In one aspect, thealgorithm can be trained with known samples and thereafter validatedwith samples of known identity. As used herein, the term “learningstatistical classifier system” includes a machine learning algorithmictechnique capable of adapting to complex data sets (e.g., panel ofmarkers of interest and/or list of IBS-related symptoms) and makingdecisions based upon such data sets. The learning statistical classifiersystem can be selected from the group consisting of a random forest(RF), classification and regression tree (C&RT), boosted tree, neuralnetwork (NN), support vector machine (SVM), general chi-squaredautomatic interaction detector model, interactive tree, multiadaptiveregression spline, machine learning classifier, and combinationsthereof. Preferably, the learning statistical classifier system is atree-based statistical algorithm (e.g., RF, C&RT, etc.) and/or a NN(e.g., artificial NN, etc.). Additional examples of learning statisticalclassifier systems suitable for use in the present invention aredescribed in U.S. Patent Application Publication Nos. 2008/0085524,2011/0045476 and 2012/0171672. In certain embodiments, the methodscomprise classifying a sample from the subject as an IBS sample ornon-IBS sample (e.g., sample from a healthy control).

In certain instances, the statistical algorithm is a single learningstatistical classifier system. Preferably, the single learningstatistical classifier system comprises a tree-based statisticalalgorithm such as a RF or C&RT. As a non-limiting example, a singlelearning statistical classifier system can be used to classify thesample as an IBS sample or non-IBS sample (e.g., healthy control) basedupon a prediction or probability value and the presence or level of atleast one diagnostic marker (i.e., diagnostic marker profile comprisinga bacterial antigen antibody marker profile and/or a mast cell markerprofile), alone or in combination with the presence or severity of atleast one symptom (i.e., symptom profile). The use of a single learningstatistical classifier system typically classifies the sample as an IBSsample with a sensitivity, specificity, positive predictive value,negative predictive value, and/or overall accuracy of at least about75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%,89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. As such, theclassification of a sample as an IBS sample or non-IBS sample is usefulfor aiding in the diagnosis of IBS in a subject.

In certain other instances, the statistical algorithm is a combinationof at least two learning statistical classifier systems. Preferably, thecombination of learning statistical classifier systems comprises a RFand a NN, e.g., used in tandem or parallel. As a non-limiting example, aRF can first be used to generate a prediction or probability value basedupon the diagnostic marker profile, alone or in combination with asymptom profile, and a NN can then be used to classify the sample as anIBS sample or non-IBS sample based upon the prediction or probabilityvalue and the same or different diagnostic marker profile or combinationof profiles. Advantageously, the hybrid RF/NN learning statisticalclassifier system of the present invention classifies the sample as anIBS sample with a sensitivity, specificity, positive predictive value,negative predictive value, and/or overall accuracy of at least about75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%,89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. In aparticularly preferred embodiment, the statistical algorithm is a randomforest classifier or a combination of a random forest classifier and aneural network classifier.

In some instances, the data obtained from using the learning statisticalclassifier system or systems can be processed using a processingalgorithm. Such a processing algorithm can be selected, for example,from the group consisting of a multilayer perceptron, backpropagationnetwork, and Levenberg-Marquardt algorithm. In other instances, acombination of such processing algorithms can be used, such as in aparallel or serial fashion.

The various statistical methods and models described herein can betrained and tested using a cohort of samples from healthy individualsand IBS patients. For example, samples from patients diagnosed by aphysician, and preferably by a gastroenterologist, as having IBS or aclinical subtype thereof using a biopsy, colonoscopy, or an immunoassayas described in, e.g., U.S. Pat. Publication No. 2010/0094560, aresuitable for use in training and testing the statistical methods andmodels of the present invention. Samples from patients diagnosed withIBS can also be stratified into IBS subtypes using an immunoassay asdescribed in, e.g., U.S. Pat. No. 8,463,553 and U.S. Pat. PublicationNos. 2010/0094560 and 2008/0085524. Samples from healthy individuals caninclude those that were not identified as IBS samples. One skilled inthe art will know of additional techniques and diagnostic criteria forobtaining a cohort of patient samples that can be used in training andtesting the statistical methods and models of the present invention.

J. Methods of Predicting Celiac Disease (CD)

In some embodiments, the sample from the subject is assayed to determineif it is a celiac disease sample or a non-celiac disease sample. If itis predicted to be a non-celiac disease sample, it progresses to thenext module where it is determined if the sample is an inflammatorybowel disease (IBD) sample or a non-IBD sample

In some embodiments, the method for determining whether a sample is a CDsample or a non-CD sample includes measuring the level of one or more CDmarkers, such as, but not limited to, an anti-gliadin IgA antibody, ananti-gliadin IgG antibody, an anti-tissue transglutaminase (tTG)antibody, an anti-endomysial antibody (EMA) and combinations thereof. Inother embodiments, the methods includes measuring the level of each ofthe following CD markers: an anti-gliadin IgA antibody, an anti-gliadinIgG antibody, an anti-tissue transglutaminase (tTG) antibody, and ananti-endomysial antibody (EMA).

In certain instances, the presence or absence of markers of CD isdetermined using an immunoassay or an immunohistochemical assay. Anon-limiting example of an immunoassay suitable for use in the methodsof the present invention includes an enzyme-linked immunosorbent assay(ELISA). Examples of immunohistochemical assays suitable for use in themethods of the present invention include, but are not limited to,immunofluorescence assays such as direct fluorescent antibody assays,indirect fluorescent antibody (IFA) assays, anticomplementimmunofluorescence assays, and avidin-biotin immunofluorescence assays.Other types of immunohistochemical assays include immunoperoxidaseassays. Preferably, the presence or absence of anti-gluten antibodies,anti-tTG antibodies, and anti-endomysial antibodies is eachindependently determined using an immunoassay (e.g., ELISA) orimmunohistochemical assay (e.g., IFA).

In some embodiments, the identification of subjects with CD or non-CD isbased upon the presence or absence of markers of CD in conjunction witha statistical algorithm. A detailed description of useful statisticalalgorithms is provided above.

In some embodiments, the presence of EMA and anti-tTG antibodies ispredictive of CD. In other embodiments, the presence of either EMA oranti-tTG antibodies in the absence of anti-gliadin IgA antibodies andanti-gliadin IgG antibodies is predictive of CD. In yet otherembodiments, the presence of anti-gliadin IgA antibodies or anti-gliadinIgG antibodies in the absence of EMA and anti-tTG antibodies ispredictive of non-CD. In some embodiments, the absence of EMA, anti-tTGantibodies, anti-gliadin IgA antibodies, and anti-gliadin IgG antibodiesis predictive of non-CD.

If the subject's sample is determined to be non-CD, the sample isassayed in the IBD module to predict if it is an inflammatory boweldisease sample (IBD) or a non-IBD sample.

K. Methods of Predicting Inflammatory Bowel Disease (IBD)

In some embodiments, the method for determining whether a sample is anIBD sample or a non-IBD sample includes measuring the level of one ormore IBD markers, such as, but not limited to, an anti-neutrophilcytoplasmic antibody (ANCA), an anti-Saccharomyces cerevisiaeimmunoglobulin G (ASCA-IgA), an anti-Saccharomyces cerevisiaeimmunoglobulin G (ASCA-IgG), an anti-outer membrane protein C(anti-OmpC) antibody, an anti-flagellin antibody, a perinuclearanti-neutrophil cytoplasmic antibody (pANCA), an anti-I2 antibody, ananti-Fla2 antibody, an anti-FlaX antibody, an anti-CBir antibody,ICAM-1, VCAM-1, VEGF, C-reactive protein (CRP), SAA, and combinationsthereof. Additional IBD markers include lactoferrin, anti-lactoferrinantibodies, elastase, calprotectin, hemoglobin, NOD2/CARD 15, andcombinations thereof. In other embodiments, the method also includesdetermining the genotype of each of the genetic markers ATG16L1, ECM1,NKX2-3, and STAT3. In some instances, genotyping each of the geneticmarkers includes detecting the presense or absence of a singlenucleotide polymorphism (SNP) in each of the genetic markers, such asrs2241880 for ATG16L1, rs3737240 for ECM1, rs10883365 for NKX2-3, and/orrs744166 for STAT3.

In certain instances, the presence or level of at least one marker isdetermined using an immunoassay or an immunohistochemical assay. Anon-limiting example of an immunoassay suitable for use in the method ofthe present invention includes an enzyme-linked immunosorbent assay(ELISA). Examples of immunohistochemical assays suitable for use in themethod of the present invention include, but are not limited to,immunofluorescence assays such as direct fluorescent antibody assays,indirect fluorescent antibody (IFA) assays, anticomplementimmunofluorescence assays, and avidin-biotin immunofluorescence assays.Other types of immunohistochemical assays include immunoperoxidaseassays.

Detailed description of methods for predicting inflammatory boweldisease are found in, e.g., U.S. Pat. Nos. 7,873,479; 8,315,818; and8,715,943 and U.S. Patent Publication No. 2013/0225439, the disclosuresof which are hereby incorporated by reference for all purposes.

IV. EXAMPLES

The following examples are offered to illustrate, but not to limit, theclaimed invention.

Example 1 Diagnostic Method for Predicting Irritable Bowel Syndrome(IBS)

IBS is a heterogeneous disease with a vast mix of pathophysiology. Toaccurately diagnose IBS it is necessary to assay biomarker levels fromthe following seven classes: inflammatory bowel disease biomarkers(e.g., ANCA, ASCA, Cbir1, FlaX, etc.), mast cell markers (e.g.,β-tryptase, PGE2 and histamine), microbiome markers (e.g., antibodiesagainst, for example, Fla1, Fla2, FlaA, FliC, FliC2, FliC3, EcFliC,Ec0Flic, SeFljB, CjFlaA, CjFlaB, SJFliC, CjCgtA, Cjdmh, CjGT-A, EcYidX,EcEra, EcFrvX, EcGabT, EcYedK, EcYabN, EcYhgN, RtMaga, RbCpaF, RgPilD,LaFrc, LaEno, LjEFtu, BfOmpA, PrOmpA, Cp10bA, CpSpA, EfSant, LmOsp,STET-2, Cpatox, Cpbtox, etc.), markers of the kynurenine pathway (e.g.,KA, 3-OHK, QA, and 3-OHAA), markers of the serotonin pathway (e.g.,5-HT, 3-HIAA, 5-HTP and 3-HK), markers of the bile acid malabsorptionpathway (e.g., 7α-hydroxy-4-choleston-3-one, and FGF19), andinflammatory markers (e.g., CRP, ICAM, VCAM, SAA, etc.).

This example illustrates a method for predicting IBS in an individualbased on the biomarker scores of several diagnostic biomarker modules.See, FIG. 5. Each score is algorithmically derived from the presence orlevel (e.g., concentration) of at least one biomarker in a sample fromthe individual. The IBS diagnostic method uses measurements from atleast 6 biomarker modules to compute an IBS score based on a statisticalalgorithm (e.g., decision tree method or random forest algorithm) forpredicting IBD vs. non-IBD.

A first random model is used to determine if a patient's sample is aceliac disease (CD) or a non-celiac disease sample (105). If the scoreis higher (greater) than the CD vs. non-CD cut-off, the sample ispredicted to be from a patient having CD, i.e., a CD sample (108).Otherwise, the sample is predicted to be from a patient having non-CD(110). The non-CD samples proceed to the next step of the algorithm,e.g., predicting IBD vs. non-IBD (120). The CD score utilizedmeasurements of CD markers such as anti-gliadin IgA antibody,anti-gliadin IgG antibody, anti-tissue transglutaminase (tTG) antibody,and anti-endomysial antibody.

Another random model is used to determine if a patient's sample is anIBD or a non-IBD sample (120). The inflammatory bowel disease (IBD)score uses measurements of serology markers such as ANCA, ASCA-A,ASCA-G, FlaX, Fla2, pANCA, OmpC, CBir1, and combinations thereof. If thescore is higher (greater) than the IBD vs. non-IBD cut-off, the sampleis predicted to be from a patient having IBD, i.e., an IBD sample (123).Otherwise, the sample is predicted to be from a patient having non-IBD(125).

Samples predicted to have non-IBD, proceed to the next step of thealgorithm, which is a decision tree or set of rules designed to rule inIBS (130). The IBS rules are based on one or more of 6 biomarkermodules, including the kynurenine (140), mast cell (150), serotonin(160), bile acid malabsorption (170), microbiome (180) and inflammatorymodules (190). The oxidative stress score (145) uses measurements fromthe kynurenine pathway, the tryptophan pathway and metabolites thereof,such as kynurenine (K), kynurenic acid (KA), 3-hydroxykynurenine (3-OHKor 3-HK), 3-hydroxyanthranilicacid (3-OHAA), quinolinic acid (QA),anthranilic acid (AA), 5-hydroxytryptophan (5-HTP) and3-hydroxykynurenine (3-HK). The mast cell score (155) is based on thelevel of mast cell markers such as β-tryptase, prostaglandin E2, andhistamine. The serotonin score (165) uses measurements from theserotonin pathway and metabolites thereof, including serotonin (5-HT),5-hydroxyindole acetic acid (5-HIAA), serotonin-O-sulfate, andserotonin-O-phosphate. The bile acid malabsorption (BAM) score (175) isderived from the level (e.g., concentration) of BAM markers such as7-α-hydroxycholesten-3-one and FGF19. The microbiome score (185) isdetermined from measurements of bacterial antigen antibodies includingthose against bacterial antigens, such as Fla1, Fla2, FlaA, FliC, FliC2,FliC3, YBaN1, ECFliC, Ec0FliC, SeFljB, CjFlaA, CjFlaB, SJFliC, CjCgtA,Cjdmh, CjGT-A, EcYidX, EcEra, EcFrvX, EcGabT, EcYedK, EcYbaN, EcYhgN,RtMaga, RbCpaF, RgPilD, LaFrc, LaEno, LjEFTu, BfOmpa, PrOmpA, Cp10bA,CpSpA, EfSant, LmOsp, STET-2, Cpatox, Cpbtox, EcSta2, Ec0Stx2A,CjcdtB/C, CdtcdA/B, and combinations thereof. The inflammatory score(195) uses measured levels of inflammatory markers such as acute phaseproteins, e.g., CRP and SAA, and immunoglobulin proteins, e.g., ICAM-1,VCAM-1.

If the sample matches the pattern for the IBS rules, the algorithmpredicts that the sample is IBS. Otherwise, the sample is predicted tobe from a healthy patient. In other words, if the score is less than theIBS vs. healthy cut-off, the algorithm predicts the sample as havingnon-IBS. If the score is greater than the cut-off, the algorithmpredicts the sample as having IBS. The IBS score can also be used toclassify the sample as an IBS-constipation (IBS-C), IBS-diarrhea(IBS-D), IBS-mixed (IBS-M), IBS-alternating (IBS-A), or post-infectiousIBS (IBS-PI) sample.

This method integrates expression data from different biomarker modulesincluding the IBD score, oxidative stress (kynurenine) score, mast cellscore, serotonin score, bile acid malabsorption (BAM) score, microbiomescore, and inflammatory score to generate a predictive index (profile,score, and the like) that can be compared to a standardized diagnosticscale or look-up table.

Example 2 Calculating a Microbiome Score

This example illustrates a method for identifying predictive bacterialantigen antibody biomarkers (e.g., microbiome markers) that areindicative of IBS. This example also shows that these biomarkers can beused to determine if a sample is from a patient having IBS.Additionally, the example illustrates a method for calculating amicrobiome score.

In this study, the level of antibodies against bacterial antigens wasmeasured in samples from healthy controls and patients diagnosed ashaving IBS (e.g., IBS-D/M). The bacterial antigen antibodies includedantibodies against bacterial antigen such as, EcFliC, Ec0Flic, SeFljB,CjFlaA, CjFlaB, SJFliC, CjCgtA, Cjdmh, CjGT-A, EcYidX, EcEra, EcFrvX,EcGabT, EcYedK, EcYabN, EcYhgN, RtMaga, RbCpaF, RgPilD, LaFrc, LaEno,LjEFtu, BfOmpA, PrOmpA, Cp10bA, CpSpA, EfSant, LmOsp, STET-2, Cpatox,and Cpbtox. There were approximately 200 healthy control samples and 200IBS-D/M samples analyzed. The level of at least one inflammatory markerand at least one mast cell marker also measured. The presence or level(e.g., concentration) of the biomarkers were determined using methodssuch as amplification-based assays, such as PCR, hybridization assays,such as an ELISA, competitive ELISA, and CEER™ or immunohistochemicalassay, or mobility assays such as separation chromatography, HPLC, orHMSA. Detailed descriptions of useful assays are found in, e.g., U.S.Pat. Nos. 8,278,057 and 8,114,616; U.S. Patent Publication Nos.2012/0244558 and 2012/0315630.

A logistic regression model was used to identify markers that showed astatistically significant difference between healthy control and IBSpatients. Table 2 shows the results.

TABLE 2 Estimate Std. Error z value Pr(>|z|) Intercept 1.6916 0.49573.4123 0.0006 *** CjFlaA −1.1594 0.5771 −2.0091 0.0445 * CjFlaB 0.83890.4305 1.9484 0.0514 . CjGT.A −0.7189 0.3188 −2.2549 0.0241 * EcEra3.9686 0.7035 5.6417 0.0000 *** EcGabT −4.4100 0.7601 −5.8015 0.0000 ***EcOFliC −1.5502 0.2244 −6.9073 0.0000 *** EcYbaN 2.8258 0.6672 4.23520.0000 *** SeFljB −1.4180 0.5912 −2.3987 0.0165 * SfFlic 1.0425 0.21514.8472 0.0000 ***

Of the 1000 iterations that were run, ⅔ were for the training set and ⅓were for the validation set. FIG. 6A shows a ROC AUC of 0.843 whenbacterial antigen markers and an inflammatory marker were analyzed. FIG.6B shows a ROC AUC of 0.9 when bacterial antigen markers, aninflammatory marker and a mast cell marker were evaluated. The datashows that microbiome markers in combination with at least oneinflammatory marker are predictive of IBS. Furthermore, the addition ofat least one mast cell marker is also predictive of IBS over healthycontrol.

To understand the interactions between markers and biomarker classes inthe model algorithm, decision trees were used. The steps of thetree-building process included 1) identifying a marker that bestdifferentiates IBS patients from healthy controls, for instance, bylowest p-value; 2) identifying a marker cut-off value that bestseparates (distinguishes) IBS patients from healthy controls, and thenmoving on to the next node of the decision tree and repeating steps 1and 2 (FIG. 7).

A microbiome score and percentile score were calculated for the patientsdiagnosed as having IBS and healthy controls. The levels of antibodiesagainst the following bacterial antigens were measured: EcEra, EcFliC,EcFrvX, EcGabT, EcYedK, EcYbaN, EcOFliC, CjFlaA, CjFlaB, CjGTA, CjCgtA,Cjdmh, SeFljB and SfFliC (FIG. 8A-8N).

The individual's score was calculated by generating a weighted quartilesum score. Using a logistic regression model of disease status such ashealthy versus IBS for all the markers, the coefficients from regressionor slope were determined. A positive slope shows that the marker ispredictive of IBS and a negative slope indicates that the marker ispredictive of healthy status (FIGS. 9A and 9B, respectively). Thecoefficients were adjusted for the presence of other markers. Healthycontrols were used to obtain quartile cut-offs (FIG. 9C). For eachindividual, the microbiome score=Σβ*quartile over all markers analyzed,wherein β represents the coefficients from the regression or slopebetween the disease cohorts (FIG. 9C).

FIGS. 10A and B show graphs of the microbiome score (FIG. 10A) andmicrobiome score percentile (FIG. 10B) for the healthy control cohort.The graphs also show the microbiome score of one representative IBSpatient relative to the control cohort.

FIGS. 11A and B show graphs of the microbiome score (FIG. 11A) andmicrobiome score percentile (FIG. 11B) for the healthy control cohortand the IBS-D/M patient cohort. The results show that IBS-D/M patientshave a higher microbiome score than healthy controls.

The method described herein for calculating a microbiome score andestablishing quartiles can be used as an exemplary model for determiningother module scores, e.g., an IBD score, oxidative stress score, mastcell score, serotonin score, BAM score, and inflammatory score.

Example 3 Predictive Microbiome Markers for IBS

This example shows that microbiome markers (e.g., antibodies againstbacterial antigens) are predictive of IBS. The example provides acomparison of biomarker levels in healthy controls and patients withIBS-D/M.

Serum samples from healthy controls and patients with IBS-D/M wereobtained and levels of antibodies against the bacterial antigens listedin Table 1 (see, above) were measured using an ELISA method.

Microbiome markers were analyzed in 3 patient cohorts (#1-3). For cohort#1, there was no difference in the levels of anti-LaEno antibody (FIG.12A), anti-LaFrc antibody (FIG. 12B), and anti-LjEFTu antibody (FIG.12C) in the healthy controls (n=295) vs. the IBS-D/M patients (n=229).The level of anti-BJOmpA antibody (FIG. 12D) was also similar betweenthe healthy controls (n=96) and the IBS patients (n=104). Foranti-PrOmpA antibody, there was a difference between the two groups(p<0.0232; FIG. 12E). In particular, the IBS patients had a lower levelof the PrOmpA marker. For cohort #2, the levels of anti-EcGabT antibody(FIG. 13A), anti-EcEra antibody (FIG. 13B), anti-SfFliC antibody (FIG.13D) and anti-CjFlaB antibody (FIG. 13E) were higher in the IBS group.No difference was detected for the anti-Ec0FliC (FIG. 13C), anti-CjFlaA(FIG. 13F), anti-EcFliC, (FIG. 13G), anti-RtMaga (FIG. 13H), anti-RgPilD(FIG. 13I), anti-RbCpaF (FIG. 13J) antibodies. There was a statisticaldifference in anti-RbCpaF antibody levels if the healthy control orIBS-C patients was compared to the IBS-D patients (FIG. 13K). With thismarker, the IBS-D patients had higher levels. For cohort #3, both groupshad similar levels of the anti-CjFlaA (FIG. 14C), anti-EcFliC (FIG.14D), anti-EcGabT (FIG. 14E), and anti-EcEra (FIG. 14F) antibodies. Thelevel of anti-SfFliC (FIG. 14A), anti-CjFlaB (FIG. 14B), andanti-EcOFliC (FIG. 14G) antibodies were lower in IBS patients comparedto healthy controls.

This example shows that microbiome markers (e.g., antibodies againstbacterial antigens) can be used to distinguish an IBS patient from ahealthy control. Thus, these markers can be used in a method fordiagnosing IBS.

Example 4 Detecting Serotonin Dysfunction in IBS Patients

This example shows that patients with IBS have higher levels ofserotonin compared to healthy controls. Serotonin levels were measuredusing HPLC and a novel serotonin competitive ELISA which is described indetail in PCT application no. PCT/IB2014/061634, entitled “PathwaySpecific Assays for Predicting Irritable Bowel Syndrome Diagnosis,”filed May 22, 2014, the disclosure of which is hereby incorporated byreference in its entirety for all purposes.

Serum samples from healthy controls and patients with IBS-diarrhea(IBD-D) were obtained and derivatized to stabilize serotonin andmetabolites thereof. Briefly, 50 μl of the sample was incubated with 50μl of derivatization mix at 37° C. for 30 minutes. The derivatizationmix contained 0.1 M CAPS buffer (pH11.0), 0.1 M p-(aminomethyl)benzylcompound, 0.05 M potassium hexacyanoferrate (III), and methanol at aratio of 10:11:22:23 (v:v:v:v). After the derivatization reaction, thesample was deproteinated with acetonitrile (ACM) (e.g., 1:2 v/vserum:ACN). The deproteinated sample was then centrifuged at 14,000 rpmfor 20 minutes. Afterwards, it was filtered through a 0.2 μm filter andthen injected into the HPLC column which was a reverse phase, C18column. For the method, the mobile phase included 15 mM sodium acetate,pH 4.5 with 1 mM octane sulfonic acid, sodium salt. The gradient wasgenerated using acetonitrile as solvent B and the conditions were asfollows: 20% solvent B at 0 min, 26% solvent B at 2 min, 28% solvent Bat 12 min, 80% solvent B at 12.5 min, 80% solvent B at 14.5 min, 0%solvent B at 15 min, 0% solvent B at 16.5 min. 20% solvent B at 17.5min, and 20% solvent B at 20 min. The fluorescence detection was with anexcitation of 345 nm and an emission of 480 nm. Derivatized serotoninand its derivatized metabolites were detected and separated by HPLC. Inparticular, derivatized 5-HTP, 3-HK, 5-HT, 5-HIAA, and 5-HI wereresolved into distinct, separate peaks (FIG. 15B).

Serotonin levels were higher in patients with IBS-D compared to healthycontrols (FIG. 15A). The mean level was 55±10 nM serotonin in IBS-D and33±10 nM serotonin in healthy. Quartile analysis revealed that patientsin quartile 3 (Q3) and quartile 4 (Q4) had significantly higher levelsof serotonin (64.9 nM and 140.6 nM, respectively). Unlikely healthycontrols, these IBS-D patients displayed serotonin dysfunction.

Serotonin levels were also measured using a competitive ELISA. Abiotinylated, derivatized serotonin (e.g., Ser-D) analog was coated ontoa streptavidin plate. The serum sample was derivatized as describedabove and incubated with a novel anti-Ser-D antibody generated inrabbits (see, PCT application no. PCT/IB2014/061634, entitled “PathwaySpecific Assays for Predicting Irritable Bowel Syndrome Diagnosis,”filed May 22, 2014. The sample mixture was added to the plate andincubated for 1 hour at RT. The plate was washed several times with washbuffer. A goat anti-rabbit antibody-HRP conjugate solution was added andincubated for 1 hour at RT. The plate was washed several times in washbuffer. A color substrate was added for the colorimetric reaction andstop solution was added prior to reading the plate at 405 nm. Serotoninlevels from IBS-D patients are shown in FIG. 16A. The mean amount ofserotonin in the IBS-D patients was 50±20 nM compared to 23±10 nM inhealthy controls (FIG. 16B). Quartile analysis also showed that patientsin quartiles 3 and 4 had significantly high levels compared to thehealthy controls. The ELISA data supports the findings of the HPLCmethod. The experiments demonstrate that patients with IBD-D experienceserotonin dysfunction. Thus, serotonin and metabolites thereof can serveas predictive indicators of IBS-D.

All publications and patent applications cited in this specification areherein incorporated by reference as if each individual publication orpatent application were specifically and individually indicated to beincorporated by reference. Although the foregoing invention has beendescribed in some detail by way of illustration and example for purposesof clarity of understanding, it will be readily apparent to those ofordinary skill in the art in light of the teachings of this inventionthat certain changes and modifications may be made thereto withoutdeparting from the spirit or scope of the appended claims.

What is claimed is:
 1. A method for aiding in the diagnosis of irritablebowel syndrome (IBS) and/or a clinical subtype thereof in a subject,said method comprising: (a) detecting in a sample a first panel ofmarkers to rule-out a diagnosis of inflammatory bowel disease (IBD) andceliac disease (CD); (i) wherein ruling-out CD includes detecting insaid sample obtained from said subject the presence or absence of one ormore of the following markers: an anti-gliadin IgA antibody, ananti-gliadin IgG antibody, an anti-tissue transglutaminase (tTG)antibody, or an anti-endomysial antibody to obtain a CD score, andapplying a decision tree or a set of rules to said CD score to obtain adecision whether said sample is a CD sample or a non-CD sample; (ii)wherein ruling-out IBD includes detecting in said sample the presence orlevel or genotype of one or more of the following markers to obtain anIBD score: (A) the presence or level of a serological marker selectedfrom the group consisting of ASCA-A, ASCA-G, ANCA, pANCA, anti-OmpCantibody, anti-CBir1 antibody, anti-FlaX antibody, or anti-A4-Fla2antibody; or (B) the presence or level of an inflammation markerselected from the group consisting of VEGF, ICAM, VCAM, SAA, or CRP; or(C) the genotype of a genetic marker selected from the group consistingof ATG16L1, ECM1, NKX2-3, or STAT3; and (b) detecting in said sample asecond panel of markers, which second panel includes: (c) at least onebacterial antigen antibody marker to obtain a microbiome score, and onebile acid malabsorption marker to obtain a malabsorption score torule-in a diagnosis of IBS.
 2. The method of claim 1, wherein saidmethod further comprises obtaining one or more of the following (a)-(i)scores: (a) detecting in said sample the level of at least one mast cellmarker to obtain a mast cell score; or (b) detecting in said sample thelevel of at least one inflammatory cell marker to obtain an inflammatoryscore; or (c) detecting in said sample the level of at least one bileacid malabsorption (BAM) marker to obtain a BAM score; or (d) detectingin said sample the level of at least one kynurenine marker to obtain anoxidative stress score; or (e) detecting in said sample the level of atleast one serotonin marker to obtain a serotonin score; or (f) if saidsample is a non-CD sample, then applying a random forest statisticalanalysis to said IBD score to obtain a decision whether the sample is anIBD sample or a non-IBD sample; or (g) if said sample is a non-IBDsample, then applying a statistical algorithm to one or more of thefollowing: said microbiome score, said mast cell score, saidinflammatory score, said BAM score, said oxidative stress score, andsaid serotonin score to obtain a disease score; or (h) determining adiagnosis of IBS in said subject based on a statistical algorithm thatgenerates a probability of having IBS based on the disease score and adiagnostic model comprising a microbiome score, a mast cell score, aninflammatory score, a bile acid malabsorption score, an oxidative stressscore, and a serotonin score from a retrospective cohort of patients. 3.The method of claim 1, wherein the at least one bacterial antigenantibody marker is selected from the group consisting of an anti-Fla1antibody, anti-Fla2antibody, anti-FlaA antibody, anti-FliC antibody,anti-FliC2antibody, anti-FliC3antibody, anti-YBaN1antibody, anti-ECFliCantibody, anti-Ec0FliC antibody, anti-SeFljB antibody, anti-CjFlaAantibody, anti-CjFlaB antibody, anti-SfFliC antibody, anti-CjCgtAantibody, anti-Cjdmh antibody, anti-CjGT-A antibody, anti-EcYidXantibody, anti-EcEra antibody, anti-EcFrvX antibody, anti-EcGabTantibody, anti-EcYedK antibody, anti-EcYbaN antibody, anti-EcYhgNantibody, anti-RtMaga antibody, anti-RbCpaF antibody, anti-RgPilDantibody, anti-LaFrc antibody, anti-LaEno antibody, anti-LjEFTuantibody, anti-BfOmpa antibody, anti-PrOmpA antibody, anti-Cp10bAantibody, anti-CpSpA antibody, anti-EfSant antibody, anti-LmOspantibody, anti-SfET-2 antibody, anti-Cpatox antibody, anti-Cpbtoxantibody, anti-EcSta2antibody, anti-Ec0Stx2A antibody, anti-CjcdtB/Cantibody, anti-CdtcdA/B antibody, and combinations thereof.
 4. Themethod of claim 2, wherein the at least one mast cell marker is selectedfrom the group consisting of β-tryptase, histamine, prostaglandinE2(PGE2), and combinations thereof.
 5. The method of claim 2, whereinthe at least one inflammatory marker is selected from the groupconsisting of CRP, ICAM, VCAM, SAA, GROα, and combinations thereof. 6.The method of claim 2, wherein the at least one bile acid malabsorptionmarker is selected from the group consisting of7α-hydroxy-4-cholesten-3-one, FGF19, and a combination thereof.
 7. Themethod of claim 2, wherein the at least one kynurenine marker isselected from the group consisting of kynurenine (K), kynurenic acid(KyA), anthranilic acid (AA), 3-hydroxykynurenine (3-HK),3-hydroxyanthranilic acid (3-HAA), xanthurenic acid (XA), quinolinicacid (QA), tryptophan, 5hydroxytryptophan (5-HTP), and combinationsthereof.
 8. The method of claim 2, wherein the at least one serotoninmarkers is selected from the group consisting of serotonin (5-HT),5-hydroxyindoleacetic acid (5-HIAA), serotonin-O-sulfate,serotonin-O-phosphate, and combinations thereof.
 9. The method of claim2, wherein the diagnostic model is established using a retrospectivecohort with known outcomes of IBS and healthy controls.
 10. The methodof claim 2, wherein the diagnostic model is established using aretrospective cohort with known outcomes of a clinical subtype of IBSand healthy controls.
 11. The method of claim 2, wherein the methodfurther comprises classifying a diagnosis of IBS as IBS-constipation(IBS-C), IBS diarrhea (IBS-D), IBS-mixed (IBS-M), IBS-alternating(IBS-A), or post-infectious (IBS-PI).
 12. The method of claim 2, whereinthe level of said mast cell marker, said inflammatory cell marker, saidBAM marker, said kynurenine marker or said serotonin marker isindependently detected with a hybridization assay, amplification-basedassay, immunoassay, immunohistochemical assay, or a mobility assay. 13.The method of claim 12, wherein the hybridization assay comprises anELISA or a collaborative enzyme enhanced reactive-immunoassay.
 14. Themethod of claim 2, wherein the sample is selected from the groupconsisting of whole blood, plasma, serum, saliva, urine, stool, tears,any other bodily fluid, a tissue sample, and a cellular extract thereof.15. The method of claim 14, wherein the sample is serum.
 16. The methodof claim 2, wherein at least two members selected from the followinggroup are measured: microbiome score, a mast cell score, an inflammatoryscore, a bile acid malabsorption score, an oxidative stress score, and aserotonin score.
 17. The method of claim 16, wherein at least threemembers selected from the following group are measured: microbiomescore, a mast cell score, an inflammatory score, a bile acidmalabsorption score, an oxidative stress score, and a serotonin score.18. The method of claim 16, wherein at least four members selected fromthe following group are measured: microbiome score, a mast cell score,an inflammatory score, a bile acid malabsorption score, an oxidativestress score, and a serotonin score.
 19. The method of claim 16, whereinat least five members selected from the following group are measured:microbiome score, a mast cell score, an inflammatory score, a bile acidmalabsorption score, an oxidative stress score, and a serotonin score.20. The method of claim 16, wherein all members of the following groupare measured: microbiome score, a mast cell score, an inflammatoryscore, a bile acid malabsorption score, an oxidative stress score, and aserotonin score.